algorithmic management

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Uberland: How Algorithms Are Rewriting the Rules of Work by Alex Rosenblat

"Susan Fowler" uber, Affordable Care Act / Obamacare, Airbnb, algorithmic management, Amazon Mechanical Turk, autonomous vehicles, barriers to entry, basic income, big-box store, bike sharing, Black Lives Matter, business logic, call centre, cashless society, Cass Sunstein, choice architecture, cognitive load, collaborative economy, collective bargaining, creative destruction, crowdsourcing, data science, death from overwork, digital divide, disinformation, disruptive innovation, don't be evil, Donald Trump, driverless car, emotional labour, en.wikipedia.org, fake news, future of work, gender pay gap, gig economy, Google Chrome, Greyball, income inequality, independent contractor, information asymmetry, information security, Jaron Lanier, Jessica Bruder, job automation, job satisfaction, Lyft, marginal employment, Mark Zuckerberg, move fast and break things, Network effects, new economy, obamacare, performance metric, Peter Thiel, price discrimination, proprietary trading, Ralph Waldo Emerson, regulatory arbitrage, ride hailing / ride sharing, Salesforce, self-driving car, sharing economy, side hustle, Silicon Valley, Silicon Valley ideology, Skype, social software, SoftBank, stealth mode startup, Steve Jobs, strikebreaker, TaskRabbit, technological determinism, Tim Cook: Apple, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, union organizing, universal basic income, urban planning, Wolfgang Streeck, work culture , workplace surveillance , Yochai Benkler, Zipcar

The company may deactivate drivers who try to build their own client lists, and many drivers with whom I broach this subject say they don’t even bother trying because they feel safer knowing they are covered by Uber’s insurance policy (which offers $1 million in auto liability insurance per accident if one occurs between the time a driver accepts a trip and the trip’s completion).7 They are penalized if they decline passengers, but Uber doesn’t actually give them the information they need to assess whether a ride is profitable in advance. And Uber perennially, and unilaterally, changes their pay rates, usually by cutting them. Drivers are supposedly free and independent, but Uber’s rules, enforced by these algorithmic managers, significantly limit the opportunities for entrepreneurial decision making available to them. Drivers have noticed the tension between the promise of freedom and the reality of invasive algorithmic management. In fact, this tension is the basis of legal claims that drivers should not be classified as independent contractors.8 One of the fascinating aspects of Uber’s approach is that according to the company, its drivers are not workers at all—they are “consumers” of Uber’s technology services, just as passengers are.

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LET’S TAKE A RIDE As a technology company in the ridehail business, Uber has an employment model that is changing the nature of work. The company promised to leverage its technology to provide mass entrepreneurship to independent workers. At Uber, algorithms manage how much drivers are paid, where and when they work, and the eligibility requirements for their employment. But the power of algorithmic management is obscured from view, hidden within the black box of the app’s design. While speaking with hundreds of drivers, culling thousands of forum posts online, and working together with scholars across disciplines to suss out the implications of what I’ve observed, I’ve found that the technology practices Uber implements (such as algorithms) significantly shape and control how drivers behave at work.

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It’s difficult to distinguish between biases in society that are reflected back to us through search results, and algorithmic management practices that these companies use to manipulate users with information and inferences. But services like Google’s search engine and Facebook’s newsfeed are free, so consumers can’t easily complain if the algorithms behind them are not neutral. As the rationale goes, unhappy users should just stop using these sites if they don’t like them (although much evidence suggests that, in practice, it is difficult to opt out of using these platforms in everyday life).3 At Uber, however, the stakes are inherently higher, as algorithmic management affects the livelihoods of drivers.

Riding for Deliveroo: Resistance in the New Economy by Callum Cant

Airbnb, algorithmic management, call centre, capitalist realism, collective bargaining, deskilling, Elon Musk, fixed-gear, future of work, gamification, gig economy, housing crisis, illegal immigration, independent contractor, information asymmetry, invention of the steam engine, machine readable, Mark Zuckerberg, means of production, new economy, Pearl River Delta, race to the bottom, ride hailing / ride sharing, scientific management, sharing economy, Silicon Valley, strikebreaker, tech worker, union organizing, Winter of Discontent, women in the workforce

In return, they get premium wages and conditions. The dispatcher plays a key role in implementing the whole system of control. At Deliveroo, the labour process is actually pretty similar to other kinds of courier work. The key difference is in the chain of command, as a result of ‘algorithmic management’.5 Under algorithmic management, the role of the dispatcher is transformed. In practice, algorithmic management is the partial automation of supervision and labour process coordination through the use of information technology. The highest level of manager left in a city is a ‘driver lead’, an almost-manager, who is relegated to acting as a problem-solver.

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Deliveroo’s automation of management fits this same general pattern of technological development for the good of bosses not workers, despite the dramatically different circumstances. Algorithmic management, like a system of deskilled factory labour, is designed to further the exploitation of labour-power to provide a competitive advantage to the bosses who invest in it. There are four specific aspects of the competitive advantage gained via Deliveroo’s system of algorithmic management. First, it increases the possible complexity of labour process coordination at lower cost. Algorithms are better at multi-factor calculation and planning of the labour process than human dispatchers, and, given the adaptive potential introduced by machine learning, they can get better at it over time.

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On top of that, they often develop favourite riders who they give preferential treatment to. This process of favouritism clearly functions as a disciplinary mechanism, so has potential uses for management, but it also causes inefficiencies. With algorithmic management, a manger can eliminate error and implement a more efficient system of favouritism and victimization. Fourth, algorithmic management can be replicated in lots of new locations for little or no additional cost. The function of the app can be supervised from a central, continental office rather than local dispatch offices. As a result, Deliveroo is very light on its feet.

Futureproof: 9 Rules for Humans in the Age of Automation by Kevin Roose

"World Economic Forum" Davos, adjacent possible, Airbnb, Albert Einstein, algorithmic bias, algorithmic management, Alvin Toffler, Amazon Web Services, Atul Gawande, augmented reality, automated trading system, basic income, Bayesian statistics, Big Tech, big-box store, Black Lives Matter, business process, call centre, choice architecture, coronavirus, COVID-19, data science, deep learning, deepfake, DeepMind, disinformation, Elon Musk, Erik Brynjolfsson, factory automation, fake news, fault tolerance, Frederick Winslow Taylor, Freestyle chess, future of work, Future Shock, Geoffrey Hinton, George Floyd, gig economy, Google Hangouts, GPT-3, hiring and firing, hustle culture, hype cycle, income inequality, industrial robot, Jeff Bezos, job automation, John Markoff, Kevin Roose, knowledge worker, Kodak vs Instagram, labor-force participation, lockdown, Lyft, mandatory minimum, Marc Andreessen, Mark Zuckerberg, meta-analysis, Narrative Science, new economy, Norbert Wiener, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off-the-grid, OpenAI, pattern recognition, planetary scale, plutocrats, Productivity paradox, QAnon, recommendation engine, remote working, risk tolerance, robotic process automation, scientific management, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, social distancing, Steve Jobs, Stuart Kauffman, surveillance capitalism, tech worker, The Future of Employment, The Wealth of Nations by Adam Smith, TikTok, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, warehouse robotics, Watson beat the top human players on Jeopardy!, work culture

Classification: LCC QA76.9.C66 R635 2020 (print) | LCC QA76.9.C66 (ebook) | DDC 303.48/34—dc23 LC record available at https://lccn.loc.gov/​2020001669 LC ebook record available at https://lccn.loc.gov/​2020001670 Ebook ISBN 9780593133354 randomhousebooks.com Book design by Susan Turner, adapted for ebook Cover design: Rachel Gogel ep_prh_5.6.1_c0_r0 Contents Cover Title Page Copyright Epigraph Introduction Part I: The Machines Chapter One: Birth of a Suboptimist Chapter Two: The Myth of the Robot-Proof Job Chapter Three: How Machines Really Replace Us Chapter Four: The Algorithmic Manager Chapter Five: Beware of Boring Bots Part II: The Rules Rule 1: Be Surprising, Social, and Scarce Rule 2: Resist Machine Drift Rule 3: Demote Your Devices Rule 4: Leave Handprints Rule 5: Don’t Be an Endpoint Rule 6: Treat AI Like a Chimp Army Rule 7: Build Big Nets and Small Webs Rule 8: Learn Machine-Age Humanities Rule 9: Arm the Rebels Dedication Acknowledgments Appendix: Making a Futureproof Plan Reading List Notes By Kevin Roose About the Author Proceed as way opens.

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It’s almost certain that some of the technologies in our lives today will end up costing humans their jobs, just as these tools did. An easy lesson to draw from history is that machines disrupt our lives in ways we don’t see coming. We worry about Skynet, not spreadsheets. And when the change arrives, we’re often caught by surprise. Four The Algorithmic Manager I felt so stifled, my brain wasn’t needed anymore. You just sit there like a dummy and stare at the damn thing. I’m used to being in control, doing my own planning. Now I feel like someone else has made all the decisions for me. I feel downgraded. —worker at a recently automated General Electric plant in 1970 Every weekday, Conor Sprouls goes to work as a customer service representative for MetLife at a call center in Warwick, Rhode Island.

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IBM has used Watson, its AI platform, in employee performance reviews—meaning that your bonus might be determined not simply by how you did last year, but by how the algorithm predicts you’ll do next year. On-demand platforms like Uber and Lyft have dispensed with the idea of human supervision altogether, putting decisions like pay, dispatching, and dispute resolution in the hands of algorithms. Algorithmic management has become a lucrative industry. In addition to Cogito, there are also retail-oriented AI companies like Percolata, a Silicon Valley start-up that counts Uniqlo and 7-Eleven among its clients, which uses in-store sensors to calculate a “true productivity” score for each worker. Another AI start-up, Beqom, automates the process of calculating worker pay and year-end bonuses.

Internet for the People: The Fight for Our Digital Future by Ben Tarnoff

4chan, A Declaration of the Independence of Cyberspace, accounting loophole / creative accounting, Alan Greenspan, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic management, AltaVista, Amazon Web Services, barriers to entry, Bernie Sanders, Big Tech, Black Lives Matter, blue-collar work, business logic, call centre, Charles Babbage, cloud computing, computer vision, coronavirus, COVID-19, decentralized internet, deep learning, defund the police, deindustrialization, desegregation, digital divide, disinformation, Edward Snowden, electricity market, fake news, Filter Bubble, financial intermediation, future of work, gamification, General Magic , gig economy, God and Mammon, green new deal, independent contractor, information asymmetry, Internet of things, Jeff Bezos, Jessica Bruder, John Markoff, John Perry Barlow, Kevin Roose, Kickstarter, Leo Hollis, lockdown, lone genius, low interest rates, Lyft, Mark Zuckerberg, means of production, Menlo Park, natural language processing, Network effects, Nicholas Carr, packet switching, PageRank, pattern recognition, pets.com, profit maximization, profit motive, QAnon, recommendation engine, rent-seeking, ride hailing / ride sharing, Sheryl Sandberg, Shoshana Zuboff, side project, Silicon Valley, single-payer health, smart grid, social distancing, Steven Levy, stock buybacks, supply-chain management, surveillance capitalism, techlash, Telecommunications Act of 1996, TikTok, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, undersea cable, UUNET, vertical integration, Victor Gruen, web application, working poor, Yochai Benkler

The operator entered the relevant data and the program returned the load plan. It took thirty seconds. Doing everything on paper took days. This may have been the first time in history that a computer, speaking to another computer through the internet, told somebody what to do. It was an example of what we would now call “algorithmic management,” and it shows that the purpose of linking the mainframe with the battlefield was that the mainframe could help manage the battlefield: DARPA’s pitch to the Pentagon centered on the internet’s usefulness for command and control in mobile environments. It would be many years before a truly mobile internet appeared, however.

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It would be many years before a truly mobile internet appeared, however. When it did, the possible future that briefly flickered into view on that day at Fort Bragg returned with it. As DARPA had predicted, the elasticity of the internet, its skill in conducting data across heterogeneous networks over large distances, made it a powerful tool for algorithmic management. No company has fulfilled this prediction more energetically than Uber. Founded in 2009, it would be an online mall in the mold of Google, Facebook, and Amazon—though it didn’t make a market in attention or in goods but rather in labor, matching customers who wanted a service performed with the workers who could perform it, on demand.

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When they drive, how often their rides last, how fast they’re going, how hard they hit the brakes—the app records all these data points, among many others, and transmits them to the cloud for analysis, which improves the algorithms further. The routes become more efficient. The nudges to persuade drivers to keep driving become more personalized. Algorithmic management thus enables Uber and its many “gig economy” imitators to coordinate the labor of millions of workers without the need for middle managers, and with more technical sophistication than middle managers could ever achieve. Yet this is only one advantage. The other is that, by having software rather than humans telling workers what to do, and having the software use techniques like nudges and gamification, gig companies can pretend that nobody is telling the workers what to do, and therefore that they are not really workers at all.

The Smartphone Society by Nicole Aschoff

"Susan Fowler" uber, 4chan, A Declaration of the Independence of Cyberspace, Airbnb, algorithmic bias, algorithmic management, Amazon Web Services, artificial general intelligence, autonomous vehicles, barriers to entry, Bay Area Rapid Transit, Bernie Sanders, Big Tech, Black Lives Matter, blockchain, carbon footprint, Carl Icahn, Cass Sunstein, citizen journalism, cloud computing, correlation does not imply causation, crony capitalism, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, degrowth, Demis Hassabis, deplatforming, deskilling, digital capitalism, digital divide, do what you love, don't be evil, Donald Trump, Downton Abbey, Edward Snowden, Elon Musk, Evgeny Morozov, fake news, feminist movement, Ferguson, Missouri, Filter Bubble, financial independence, future of work, gamification, gig economy, global value chain, Google Chrome, Google Earth, Googley, green new deal, housing crisis, income inequality, independent contractor, Jaron Lanier, Jeff Bezos, Jessica Bruder, job automation, John Perry Barlow, knowledge economy, late capitalism, low interest rates, Lyft, M-Pesa, Mark Zuckerberg, minimum wage unemployment, mobile money, moral panic, move fast and break things, Naomi Klein, Network effects, new economy, Nicholas Carr, Nomadland, occupational segregation, Occupy movement, off-the-grid, offshore financial centre, opioid epidemic / opioid crisis, PageRank, Patri Friedman, peer-to-peer, Peter Thiel, pets.com, planned obsolescence, quantitative easing, Ralph Waldo Emerson, RAND corporation, Ray Kurzweil, RFID, Richard Stallman, ride hailing / ride sharing, Rodney Brooks, Ronald Reagan, Salesforce, Second Machine Age, self-driving car, shareholder value, sharing economy, Sheryl Sandberg, Shoshana Zuboff, Sidewalk Labs, Silicon Valley, single-payer health, Skype, Snapchat, SoftBank, statistical model, Steve Bannon, Steve Jobs, surveillance capitalism, TaskRabbit, tech worker, technological determinism, TED Talk, the scientific method, The Structural Transformation of the Public Sphere, TikTok, transcontinental railway, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, upwardly mobile, Vision Fund, W. E. B. Du Bois, wages for housework, warehouse robotics, WikiLeaks, women in the workforce, yottabyte

The Smartphone Society examines how our individual engagement with our hand machines, for better and for worse, is part of a societal transformation—a new era of tension, uncertainty, and possibility. One final note: This is a book for everyone. I present an analysis of the pressing issues of our time—mediated interaction, oppression, surveillance, algorithmic management, neoliberal ideology, financialization—in a language and framing accessible to all readers. I adopted this approach consciously, guided by the conviction that the future of our smartphone society should be widely discussed and democratically decided. The challenges of this moment are too important to be left to software engineers, academics, and venture capitalists.

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Tech companies push this interpretation, emphasizing how they provide the digital platform—digital infrastructure that facilitates interactions (often commercial) between at least two people or groups—and you provide the hustle. Uber, for example, is adamant that it is not the employer of the roughly one million drivers worldwide who use its app to find people to ferry around. Researchers at Carnegie Mellon’s Human-Computer Interaction Institute aren’t so sure. They call the Uber arrangement “algorithmic management.”50 App workers who use these platforms to earn money don’t have easy access to a flesh-and-blood manager. Instead they interact with an algorithm—a set of exact instructions to solve a problem or perform a computation. Algorithms can be written to perform simple tasks, like adding or subtracting numbers, or complex tasks, such as playing a video or, in the case of Uber, telling the driver where to drive, paying them what they are owed, and so forth.

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Reliable data on how much pay Uber and Lyft drivers take home is hard to come by, but a recent driver-earnings survey found that drivers of Uber’s most popular service, Uber-X, made a median wage of $14.73 an hour in 2018 after tips but before gas, insurance, and repairs— substantially less than a living wage.52 Amazon warehouse workers are algorithmically managed in a different way. Each “picker” has a GPS monitor that tells her precisely which way to walk to get to the product she’s looking for and the number of seconds it should take her to get there. If she walks a different way, or takes too long, she’ll get a warning and possibly a demerit, and too many demerits can add up to dismissal.53 Amazon’s model exemplifies the steady intensification of work over the past few decades.

After the Gig: How the Sharing Economy Got Hijacked and How to Win It Back by Juliet Schor, William Attwood-Charles, Mehmet Cansoy

1960s counterculture, Airbnb, algorithmic management, Amazon Mechanical Turk, American Legislative Exchange Council, back-to-the-land, barriers to entry, bike sharing, Californian Ideology, carbon footprint, clean tech, collaborative consumption, collaborative economy, Community Supported Agriculture, COVID-19, creative destruction, crowdsourcing, deskilling, driverless car, en.wikipedia.org, financial independence, future of work, gentrification, George Gilder, gig economy, global supply chain, global village, haute cuisine, income inequality, independent contractor, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), Jean Tirole, Jeff Bezos, jitney, job satisfaction, John Perry Barlow, John Zimmer (Lyft cofounder), Kevin Kelly, Lyft, Marshall McLuhan, Mason jar, mass incarceration, Mitch Kapor, Network effects, new economy, New Urbanism, Occupy movement, peer-to-peer rental, Post-Keynesian economics, precariat, profit maximization, profit motive, race to the bottom, regulatory arbitrage, rent gap, rent-seeking, ride hailing / ride sharing, Ruby on Rails, selection bias, sharing economy, Silicon Valley, Silicon Valley ideology, Skype, smart cities, social distancing, Stewart Brand, TaskRabbit, technological determinism, technoutopianism, Telecommunications Act of 1996, The Nature of the Firm, the payments system, Tragedy of the Commons, transaction costs, transportation-network company, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, urban planning, wage slave, walking around money, Whole Earth Catalog, women in the workforce, working poor, Yochai Benkler, Zipcar

I’ve already noted that workers’ experiences are not uniform, with variation in pay rates, job satisfaction, and how they do the work. As we saw these differences playing out at individual companies, we realized that they are explained by how dependent the worker is on income from the platform to pay basic living expenses. The two dominant approaches to platform work—algorithmic management and the precariousness of independent contracting—have largely failed to account for this diversity of outcomes and its significance. That means they have also missed the underlying conditions which have led to so much diversity, which we call the “retreat from control.” In contrast to conventional employers, platforms allow workers to choose their schedules and number of hours worked, and do not directly supervise the labor process.

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The experiences of supplementals are different, as they are able to boost earnings to finance discretionary spending or savings. Situations outside the platform make all the difference. Recognizing this basic reality led us to reevaluate the two dominant theories scholars have used to understand what’s going on with platforms—algorithmic control and policies of precarity. The Algorithmic Manager Algorithms have become a pervasive feature of modern life.35 They drive search results on Google, predict outcomes in the criminal justice system, and determine access to healthcare and social services. In workplaces, “people analytics” are being used for hiring, performance evaluation, and surveillance.36 But while algorithms are capable of yielding good outcomes, they are also problematic “black boxes,” as Frank Pasquale and others have argued,37 that are known to produce racially biased outcomes and inaccurate results.

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TaskRabbits are often free to do tasks as they prefer, although in some cases clients are more directive. Ride-hail arguably offers the least amount of freedom. But even there, soft control via nudges affords workers discretion.82 Cameron finds that drivers have “contingent autonomy.”83 Alex Wood and colleagues, who studied microtasking, describe “autonomy in the shadow of algorithmic management.”84 While we find more worker sovereignty on the more lucrative platforms, the sector as a whole differs from conventional workplaces in this regard. Earners are also able to pursue divergent economic strategies without platform interference. This hasn’t been written about, but it stood out in our data.

Leadership by Algorithm: Who Leads and Who Follows in the AI Era? by David de Cremer

"Friedman doctrine" OR "shareholder theory", algorithmic bias, algorithmic management, AlphaGo, bitcoin, blockchain, business climate, business process, Computing Machinery and Intelligence, corporate governance, data is not the new oil, data science, deep learning, DeepMind, Donald Trump, Elon Musk, fake news, future of work, job automation, Kevin Kelly, Mark Zuckerberg, meta-analysis, Norbert Wiener, pattern recognition, Peter Thiel, race to the bottom, robotic process automation, Salesforce, scientific management, shareholder value, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, Stephen Hawking, The Future of Employment, Turing test, work culture , workplace surveillance , zero-sum game

Summary: The benefits of inclusive leadership Different departments to co-operate Integrating teams of data scientists in the daily operations of the company Promoting transparency in communication and exchange of data Empower algorithms in non-biased ways Be humble, and work on being tech savvy Chapter 10: What Will Be and What Should Be Too good to be true? The end user is technology, or am I wrong? What about the human? We will survive… again! Co-operation above all How to move on Building the right culture Humans lead, algorithms manage Who knows where to go? Continuous education Humanity in AI as a guiding tool Tolerance for imperfection Conclusion Publishing details Praise for Leadership by Algorithm “Everyone is talking about artificial intelligence, but no one has a clue how it will affect the way organizations are managed… until now.

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With this set-up, many believe that the ability of blockchain to provide total control will help increase feelings of safety, and, hence, trust. Isn’t that what managers should be doing in the first place? If so, technology like blockchain will indeed become part of our management systems very soon. Management by algorithm But, to answer the question of whether the algorithmic manager will wake up soon, let us return again to how we defined management. As I explained earlier, the purpose of management is to ensure that order and stability is maintained. According to our initial analysis, algorithms seem perfectly equipped to achieve this purpose. Indeed, as all the examples illustrate, algorithms penetrate managerial jobs by providing more specific and consistent ways of assessing, monitoring and evaluating employees.

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But, these same algorithms are not able to provide the authentic sense of leadership required to make decisions and subsequent meaningful changes to the humans being led. Building a culture that effects employees’ ways of thinking and acting requires a process of logic that connects with our human identity and ambitions. Humans lead, algorithms manage What this discussion makes clear is that the leadership of the future should be able to create a culture in which it becomes meaningful for humans to collaborate with non-humans. This empowerment of the new diversity requires guidance in such a way that both parties know their position within the work setting and accordingly create value that serves a society defined by humanity rather than by technological innovation.

The Gig Economy: A Critical Introduction by Jamie Woodcock, Mark Graham

Airbnb, algorithmic management, Amazon Mechanical Turk, autonomous vehicles, barriers to entry, British Empire, business process, business process outsourcing, Californian Ideology, call centre, collective bargaining, commoditize, corporate social responsibility, crowdsourcing, data science, David Graeber, deindustrialization, Didi Chuxing, digital divide, disintermediation, emotional labour, en.wikipedia.org, full employment, future of work, gamification, gender pay gap, gig economy, global value chain, Greyball, independent contractor, informal economy, information asymmetry, inventory management, Jaron Lanier, Jeff Bezos, job automation, knowledge economy, low interest rates, Lyft, mass immigration, means of production, Network effects, new economy, Panopticon Jeremy Bentham, planetary scale, precariat, rent-seeking, RFID, ride hailing / ride sharing, Ronald Reagan, scientific management, self-driving car, sentiment analysis, sharing economy, Silicon Valley, Silicon Valley ideology, TaskRabbit, The Future of Employment, transaction costs, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, union organizing, women in the workforce, working poor, young professional

In the case of Uber, the platform with its collection of data and automated back-end provides a scalability far beyond existing taxi companies. Building on this, the smartphone app has replaced the physical radio dispatcher as the interface. The use of technology – as well as huge amounts of marketing – provides powerful network effects that draw in both drivers and customers, further spurring this growth. The use of algorithmic management (Lee et al., 2015) keeps the costs low, while providing a new way to effectively manage a geographically dispersed and scalable workforce. Jobs are assigned and evaluated through code and data, without the need for human intervention. There is little chance of feedback, negotiation or the possibility of disputing decisions, resulting in very little transparency for workers.

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The issues of self-employment mean that in many contexts, workers cannot join a trade union – let alone have it recognized – or organize a legal strike. However, this also creates a kind of ‘illusion of control’ for platforms like Deliveroo (Woodcock, forthcoming). While they are able to use forms of ‘algorithmic management’ (Lee et al., 2015; Rosenblat and Stark, 2016; Rosenblat, 2018) to control the work, this becomes much harder when workers decide to resist. As Deliveroo riders were classified as self-employed, independent contractors, the regulations and laws governing strike action did not apply to them.

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., Suri, S. and Vaughan, J.W. (2016), The communication network within the crowd. Proceedings of the 25th International World Wide Web Conference (WWW), Montreal, Canada, 11 April. Index A accountability 124–30, 141 ACFTU (All-China Federation of Trade Unions) 100 Ackroyd, S. 34 Africa size of gig economy 39 algorithmic management 52 Amabile, Teresa 27 Amazon 25 Amazon Mechanical Turk 6, 43, 58, 59–60, 66, 84, 85–6, 87–8 ‘Dear Mr. Bezos’ letters 87–8, 106 Turkopticon 106–7, 123, 133 Anderson, B. 80 Antunes, Ricardo 36 application programming interfaces (API) 58 apps 5, 51, 52, 133, 138 artificial intelligence 50, 58, 60, 66 Aslam, Yaseen 76 assembly line 24, 94, 117 Australia 127 Australian Independent Contractors Act 128 automation 66–7 Avendano, Pablo 73 B Badger, Adam 86–7 Bangalore (India) 98–9, 102 Barbrook, R. 37 Barry, J. 49 Beck, Ulrich 17 Bent, P. 13, 16 Berg, J. 55 Besant, Annie 14 Bezos, Jeff 87 Bolt 77 Bourdieu, Pierre 17 ‘BrainWorkers’ 33 Braverman, Harry 111 Bryant and May match factory 14 C Californian Ideology 37 call centres 24, 31 outsourcing of 37, 54 Callinicos, Brent 49 Cameron, A. 37 Cant, Callum 40, 96 capitalism cognitive 37 gendered basis of 29 car industry 110 care work 64, 66, 79–83 low barriers to entry 67 and repeat transactions 68 Care.com 80, 80–1 casualization 5, 15 Caviar 73 ‘ChainWorkers’ 33 Cheung, Adora 103 China worker resistance and strikes 100 Christie, N. 73 cleaning work 5–6, 64 low barriers to entry 67 migrant workers 30 cloudworker platforms 6, 43, 53–61, 63, 64, 69, 93 atomization of 92 availability of 56 location of 55, 57 removal of barriers to entry for 69 and resistance 104–8 setting rates of pay 65 and spatial control 63 temporal control 64 cognitive capitalism 37 collaborations 123, 132, 136 collective bargaining 30, 34, 37, 49, 80, 130, 134, 135–6, 143 collective organization 100, 134 commercial content moderation (CCM) 61 computerization 66 consumer attitudes/preferences 27 contingent work 19 Convention on Platform Work, Draft 130, 146–51 cooperatives, platform 138–9 Countouris, N. 129 Craigslist 22 crowdsourcing 58 crowdworkers 55, 90 see also microwork; online freelancing D Dalla Costa, Mariarosa 29 Darcy, Alison 60 data collection 65–6 De Stefano, V. 129 deindustrialization 36, 84 Deliveroo 2, 6, 23, 32, 40, 71–4, 115, 127 experience of working for 7–8, 31, 71, 72–4 self-organization for workers 95 strike action 95–6, 97 delivery work(ers)/platforms 5, 27, 62, 63, 68 and automation 67 and collective organization 134 experiences of workers 71–5 low entry requirements 67 see also Deliveroo democratic ownership 136–40, 141 Denmark 3F trade union 134–5 Desai, Bhairavi 79 developing countries internet penetration rate 25 Didi Chuxing 22, 102 digital divides 25 digital legibility 23–5, 65–7 digital platforms 1, 2, 3, 4, 54–5 Directive on Transparent and Predictable Working Condition in the European Union 129 dock work(ers) 13–14, 15, 38 strike (1889) 15 domestic work(ers) 29–30, 62, 63, 66, 79–83 as central component of capitalism 29 factors determining working conditions 80 numbers 80 positive and negative outcomes for 81 and repeat transactions 68 in South Africa 81–3 Doogan, Kevin 18 E economic crisis (late 1970s) 33 Elance 22 entertainment industries 135 Eurobarometer 40 European Commission 35 Expensify 60 F Facebook 45, 60, 121, 123, 133 factories/factory work 15–16, 94 measuring of factory labour process by Taylor 23–4 Fair Crowd Work website 123 Fairwork Foundation project 121–2, 130, 146–51 Farrar, James 75, 75–6, 77–8, 101 feedback 52, 80, 92, 93 financial crisis (2008) 35 Fiverr 20, 23 flexibility, desire for by workers 4–5, 30–3, 71, 115 flexicurity 35 Flipkart 22 Foodora 127 Fordism 117 fragmented work 5, 40, 114 Freelancer 6, 54, 64, 89 freelancing, online see online freelancing Frey, C.B. 66 G gamification 86 gender and capitalism 29 and relationships of work 28–30 geographically tethered work/platforms 5–6, 7, 34, 50–2, 63 control over workforce 68 forms of resistance in 94–104 setting rates of pay 65 temporal control 64–5 Ghana 8, 64, 92 gig economy advantages 4–5 characteristics 114–15 controversy over classification of people involved 43–4 existence due to digital transformation 114 factors facilitating growth of 19, 114 five principles for ‘fair work’ in 122 future 112–45 governance in 62 meaning of 3–7 numbers working in 1–2 operation of 41–69 origins 11–40 pitfalls 5, 116 preconditions that shape the 19–28 rise of 38–40 ways to bring about change 142–4 gig economy workers barriers to entry for 67–8 communicating with each other 132–4 de-personalization of 118, 120 desire for flexibility 4–5, 31–3, 71, 115 experiences of 70–92 invisibility of 6, 80 lack of collective voice 6, 77 lack of effective regulation for 128–9 misclassified as self-employed 44 numbers 39–40 securing protection through courts 127 working conditions 6, 9 gigs, musical 3 Global North 12, 13, 32, 46 and cloudworkers 55 and microwork 84 and outsourcing 44 size of gig economy 39 Global South 32, 46 internet penetration rate 25 size of gig economy 39 women and online freelancing 90 globalization 19, 37–8 Goodwin, Tom 45, 121 Graeber, David 31 Guru.com 22 H Handy 80 Harvey, David 33, 53 Heeks, Richard 39 Herman, S. 39 Hilfr.dk 134–5 Homejoy 68, 103–4 Howe, J. 58 human intelligence tasks (HITs) 60 Humphries, S. 13–14 Hunt, A. 28, 81, 82 Huws, U. 39–40 I IAEA (International Arts and Entertainment Alliance) 135 Iles, Anthony 32 ILO (International Labour Organization) 16–17, 129 Declaration of Philadelphia (1944) 142 Independent Workers Union of Great Britain see IWGB India delivery drivers 74 strikes by Uber drivers 102 Industrial Workers of the Word see IWW industrialization 16 interface 45 International Arts and Entertainment Alliance see IAEA International Labour Organization see ILO Internet access and penetration rate 25 Irani, Lilly 106 IWGB (Independent Workers Union of Great Britain) 73, 97, 101, 109, 127, 134 IWW (Industrial Workers of the World) 97, 101 J James, Selma 29, 81 job insecurity, growth in 18–19 K Kalanick, Travis 23, 48, 49 Kalleberg, A.L. 18 Kenya Ajira Digital programme 35 Kessler, Sarah 11 L labour law 114, 117, 126, 128, 129 Lagos (Nigeria) 89, 124 Lanier, Jaron 58 LaPlante, Rochelle 60 lean platforms 35, 45 legibility, digital 23–5, 65–7 Li, Qi 100 Limer, Eric 85–6 Living Wage Foundation 122 London taxi arrangement 47 long-term unemployment 18 low-paid work, increase in 35, 139 M Machingura, F. 81, 82 McKinsey 1–2, 39 McKinsey Global Institute 66 Manila (Philippines) 89, 90 Maputo (Mozambique) 26–7 Marsh, Greg 129 Marx, Karl 11–12, 22, 72, 121 Mason, Paul 35 mass connectivity 25–7 Massey, Doreen 63 Matchwoman strike 14 Mateescu, A. 79, 80, 81 Messina, Jim 48–9 microwork 6, 55, 58–61, 62, 83–9, 104 and automation 66–7 experiences of workers 83–9 feelings of alienation 88 numbers engaged in 83–4 wages 84–5 see also Amazon Mechanical Turk 59 migrant workers 30, 80, 90 migration status 30 Mitropoulos, Angela 17, 32 mobile phones 25–6 Mondragon Corporation 138–9 Moody, Kim 40, 111 Moyer-Lee, Jason 98 N Nedelkoska, L. 66 neoliberalism 18, 33–5, 52 characteristics of 34 New York Uber 78–9 NHS (National Health Service) 5 Novogratz, Mike 49–50 O O’Connor vs Uber Technologies Inc. (2015) 124, 126 Ojanperä, Sanna 55 Ola 102 online freelancing 6, 7, 8–9, 43, 55, 62, 141 barriers to entry for workers 67 barriers to organizing 104 experiences of workers 89–92 and feedback 93 reasons for doing 89–90 support forums 104–5 wages 90, 91 and worker resistance 104–5 Osborne, M.A. 66 outcome thinking 118, 124 outsourcing 19, 37–8, 39, 44–5, 51, 54 microwork as extension of 58 P Pandor, Aisha 83 Pasha, Tanveer 102 pay rates, setting of 65 Peck, Jamie 33, 35 Peterloo Massacre (1819) 108 Platform Cooperative Consortium 138 platforms/platform work 2, 4 ability to set pay rates 65 and accountability 125–30 barriers to entry for workers 67–8 as a civic utility 139–40 cloudwork see cloudwork connecting workers and clients 20–1, 22–3, 43, 138 cooperatives 138–9 core functions 23 degree of explicit coordination 68–9 democratic ownership of 136–40, 141 digital legibility 23–5, 65–7 Draft Convention on Platform Work 130, 146–51 early 22 geographically tethered model see geographically tethered model infrastructure 20–3 intermediate function 42–3 lean 35, 45 meaning and operation of 42–6 microwork see microwork negotiation-based matching 22–3 reliance on network effects 45 repeat transactions 68 setting up of ‘counter’ 123 spatial control 62, 63–4 spatiality and temporality of 42–3 spending money on public relations and advertising 28 static-price matching 23 temporal control 64–5 understanding how they work 61–9 Plouffe, David 49 Pollman, E. 49 precariat 18 precarious work(ers) 13–19, 32–3, 38 definition 16–17 two kinds of 33 profitability, crisis of 35, 36, 42 public sector and gig economy 17 and outsourcing 44 Q Quintini, G. 66 R racialization of work 30 racism 30 ratings strategy and transparency 122–3 Ravenelle, Alexandrea 37, 70 Raw, Louise 14 Reagan, Ronald 34 reddit 123 regulation 144 lack of for gig economy workers 128–9 labour law 19, 114, 117, 126, 128 state 19, 33–6 regulatory entrepreneurship 49 repeat transactions and platforms 68 resistance see worker resistance Roberts, Sarah 61 S SAG-AFTRA 135 Samman, E. 28 Schifter, Doug 79 Scholz, Trebor 48, 49, 138, 139 Schor, Juliet 103 Screen Actors Guild (SAG) 135 Second World War 110 self-employment 32, 43–4, 96, 98, 108 Semuels, Alana 84 service industries, growth of 34 Seymour, Richard 18–19 sharing economy 11 Shekhawat, Dushyant 74 ‘shock doctrine’ 34 short term contracts 4 Silberman, Six 106 slavery 30 Slee, Tom 50, 78 soldiering 23 South Africa domestic workers in 81–3 Uber 76, 127–8 worker resistance 99–100 South African Domestic Services and Allied Workers Union (SADSAWU) 82–3 South African Labour Relations Act 128 South Korea 35 South London Gas Workers strike (1889) 14–15 Spain 127 spatial control and platforms 62, 63–4 Srnicek, Nick 4, 42, 45 standard employment relationship 5, 12–13, 16, 18, 32, 33–4 Standing, Guy 17–18, 27 state regulation 19, 33–6 strikes 14–15, 94, 95–6, 99–100, 109, 142–3 preconditions for starting 109 surveillance 24 of delivery drivers 74 Upwork workers’ resistance to 105 Susskind, R. 118 SweepSouth 80, 81–3 Switzerland Notime 102 T TaskRabbit 103 taxi industry 51–2 taxi work(ers) 75–9, 134 and collective organization 134 see also Uber Taylor, Bill 100 Taylor, Frederick 23–4 Taylor, Matthew 129 Taylor Review of Modern Working Practices, The 129 technological changes 19, 21 temporal control and platforms 64–5 temporary work(ers) 3, 17 Thatcher, Margaret 34 Thompson, S. 34 Ticona, J. 79, 80, 81 Tillett, Ben 14 tipping 75 Tolpuddle Martyrs 108–9 trade unions 6, 18, 34, 36, 92–3, 97, 108–9, 134, 135, 143–4 decline of 36, 37 and dock workers 15 early 108–9 and gig economy workers 109–10, 136 and IWGB 97 rise in membership 15 textile 108 Transnational Federation of Couriers 97 transparency 118–24, 141 establishment of ‘counter platforms’ 123 ratings strategy 122–3 Transport for London 28 Turkopticon 106–7, 123, 133 U Uber 2, 4, 20, 23, 25, 32, 44, 45, 46–50, 52, 61, 73–9, 94–5, 108, 115, 121, 124, 139 business model 48 Change.org petition 28 data collection 50, 65–6 drivers’ wages 49–50, 77–8 engagement with regulation and transport policy 48 funding 47–8 and ‘greyballing’ 49 in New York 78–9 O’Connor vs Uber Technologies Inc. (2015) 124, 126 power passengers hold over drivers 75–6 public relations and lobbying campaigns 48–9 rating system 75 safety issues and rising petrol prices for drivers in South Africa 76–7 and self-driving vehicles 50 and tipping 75 Uber International Holding(s) BV 128 Uber Technologies SA 127 UberX 47 worker resistance and strikes 100–2 unfair dismissal 44, 134 United Kingdom employment regulation issues 129 neoliberalism 34 and outsourcing 44–5 worker resistance and strikes 100–1 United Private Hire Drivers (UPHD) 75 United States neoliberalism 34 Uber 47–9 UPHD (United Private Hire Drivers) 76, 101 UpWork 6, 8, 43, 54, 64, 121 resistance of surveillance methods by workers 105 Upwork.com 89, 91 US Chamber of Commerce 108 V van Doorn, Niels 42 Vandaele, Kurt 95, 97 venture capital 36 visibility 136 vWorker 22 W wages microworkers 84–5 online freelancing 90, 91 setting of pay rates 65 Uber drivers 49–50, 77–8 Ward, H. 73 Webster, G.E. 16 Weightman, G.E. 13–14 WhatsApp 95, 99, 123, 132, 133 Williams, Eric 30 women and domestic work 29–30 and online freelancing in the Global South 90 Wood, Alex 95, 104–5, 107 work, transformation of 12–13 worker power 19, 36–7, 130–6, 141 worker resistance 93–111, 113–14 and cloudworkers 104–8 and communication 107 food platform strikes 95–7 formation of networks and meetings 95, 98–9 geographically tethered work 94–104 history of 94 legal battles over employment status 98 and online freelancing 104–5 and self-employment status 98 strikes 14–15, 94, 95–6, 99, 100–1 taking of work off-platform 103 and trade unions 97, 107–11 Uber 101–2 and WhatsApp groups 98, 99, 132 workers’ rights 34, 44, 98, 101, 130, 135, 139, 140, 144 Y YouTube 60 Z Zomato 98–9 POLITY END USER LICENSE AGREEMENT Go to www.politybooks.com/eula to access Polity’s ebook EULA.

Humans as a Service: The Promise and Perils of Work in the Gig Economy by Jeremias Prassl

3D printing, Affordable Care Act / Obamacare, Airbnb, algorithmic management, Amazon Mechanical Turk, Andrei Shleifer, asset light, autonomous vehicles, barriers to entry, call centre, cashless society, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, death from overwork, Didi Chuxing, disruptive innovation, Donald Trump, driverless car, Erik Brynjolfsson, full employment, future of work, George Akerlof, gig economy, global supply chain, Greyball, hiring and firing, income inequality, independent contractor, information asymmetry, invisible hand, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Roose, Kickstarter, low skilled workers, Lyft, machine readable, Mahatma Gandhi, Mark Zuckerberg, market friction, means of production, moral hazard, Network effects, new economy, obamacare, pattern recognition, platform as a service, Productivity paradox, race to the bottom, regulatory arbitrage, remote working, ride hailing / ride sharing, Robert Gordon, Ronald Coase, Rosa Parks, scientific management, Second Machine Age, secular stagnation, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Simon Singh, software as a service, Steve Jobs, TaskRabbit, TechCrunch disrupt, The Future of Employment, The Market for Lemons, The Nature of the Firm, The Rise and Fall of American Growth, transaction costs, transportation-network company, Travis Kalanick, two tier labour market, two-sided market, Uber and Lyft, Uber for X, uber lyft, union organizing, warehouse automation, work culture , working-age population

Nearly every aspect of on-demand work is shaped by the rating algorithms’ constant hovering over each worker like a modern-day Panoptes, the all-seeing watchman of Greek mythology: from vetting potential entrants and assign- ing tasks, to controlling how work is done and remunerated, and sanction- ing unsatisfactory performance—often without any transparency or accountability. As Judge Chen put it, citing Michel Foucault, ‘a state of conscious and permanent visibility . . . assures the automatic functioning of power’.19 Algorithmic Management ‘On the Internet,’ a 1993 New Yorker cartoon suggested, ‘nobody knows you’re a dog.’ In the on-demand economy, it seems that platforms want to know everything about their users—and their dogs. Control begins at the moment a potential worker registers with a platform, with most operators demanding extensive information and screening individual workers’ cre- dentials before activating their accounts in a process significantly more intrusive than merely signing up to yet another online service.

…

This ‘disrup- tion’ is based on contractual misclassification (the simple assertion in plat- forms’ terms and conditions that workers are independent contractors rather than employees) and the increasing use of multilateral work arrangements, * * * 96 Disrupting the Disruptors through the sharing and blurring of employer control between customers and platforms.4 Let’s look at how the law can grapple with each of these challenges. (Mis)Classifying Workers When we explored the reality of work in the on-demand economy, we saw how tight control over all aspects of service delivery was the very hallmark of algorithmic management. Employment classification should be quite straightforward, then: when a platform controls everything from which tasks are assigned to how they are performed and paid, surely we can’t speak of ‘grey areas’? Creating such uncertainty, however, is the very point of the carefully drafted and strongly worded terms and conditions that prospective workers and customers must accept before joining a platform: TASKERS ARE INDEPENDENT CONTRACTORS AND NOT EMPLOYEES OF COMPANY.

…

Rebranding Work Language Matters Peers, Neighbours, Friends Passive Platforms, Freelancing Entrepreneurs Shaping Regulation Entrepreneurship and Innovation Rethinking Employment Regulation Scrutinizing the Narratives 3. Lost in the Crowd Life as a Micro-Entrepreneur Autonomy? Algorithmic Management The Wages of Entrepreneurship Sanctions Self-Determination? Freedom? Entrepreneurship . . . or an On-Demand Trap? 4. The Innovation Paradox Nothing New under the Sun Back to the Future From Workforce to Taskforce Drawing in the Crowds The Intermediaries Struggling in the Crowd History Repeats Itself Why Should We Care?

Radical Technologies: The Design of Everyday Life by Adam Greenfield

3D printing, Airbnb, algorithmic bias, algorithmic management, AlphaGo, augmented reality, autonomous vehicles, bank run, barriers to entry, basic income, bitcoin, Black Lives Matter, blockchain, Boston Dynamics, business intelligence, business process, Californian Ideology, call centre, cellular automata, centralized clearinghouse, centre right, Chuck Templeton: OpenTable:, circular economy, cloud computing, Cody Wilson, collective bargaining, combinatorial explosion, Computer Numeric Control, computer vision, Conway's Game of Life, CRISPR, cryptocurrency, David Graeber, deep learning, DeepMind, dematerialisation, digital map, disruptive innovation, distributed ledger, driverless car, drone strike, Elon Musk, Ethereum, ethereum blockchain, facts on the ground, fiat currency, fulfillment center, gentrification, global supply chain, global village, Goodhart's law, Google Glasses, Herman Kahn, Ian Bogost, IBM and the Holocaust, industrial robot, informal economy, information retrieval, Internet of things, Jacob Silverman, James Watt: steam engine, Jane Jacobs, Jeff Bezos, Jeff Hawkins, job automation, jobs below the API, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, John von Neumann, joint-stock company, Kevin Kelly, Kickstarter, Kiva Systems, late capitalism, Leo Hollis, license plate recognition, lifelogging, M-Pesa, Mark Zuckerberg, means of production, megacity, megastructure, minimum viable product, money: store of value / unit of account / medium of exchange, natural language processing, Network effects, New Urbanism, Nick Bostrom, Occupy movement, Oculus Rift, off-the-grid, PalmPilot, Pareto efficiency, pattern recognition, Pearl River Delta, performance metric, Peter Eisenman, Peter Thiel, planetary scale, Ponzi scheme, post scarcity, post-work, printed gun, proprietary trading, RAND corporation, recommendation engine, RFID, rolodex, Rutger Bregman, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, sharing economy, Shenzhen special economic zone , Sidewalk Labs, Silicon Valley, smart cities, smart contracts, social intelligence, sorting algorithm, special economic zone, speech recognition, stakhanovite, statistical model, stem cell, technoutopianism, Tesla Model S, the built environment, The Death and Life of Great American Cities, The Future of Employment, Tony Fadell, transaction costs, Uber for X, undersea cable, universal basic income, urban planning, urban sprawl, vertical integration, Vitalik Buterin, warehouse robotics, When a measure becomes a target, Whole Earth Review, WikiLeaks, women in the workforce

However disturbingly crude they remain, to whatever extent they are all too evidently the fruit of their creators’ biases, neuroses and projections, these systems are nevertheless poised to assume responsibility for much of the work that furnishes us with a livelihood, an identity and a sense of self. And they are getting better at what they do with every passing day. What I wish to argue is that whether they are brought together consciously or otherwise, large-scale data analysis, algorithmic management, machine-learning techniques, automation and robotics constitute a coherent set of techniques for the production of an experience I call the posthuman everyday. This is a milieu in which the rhythms we contend with, the ordinary spaces we occupy, and the material and energetic flows we support are all shaped not so much by our own needs but those of the systems that nominally serve us, and in which human perception, scale and desire are no longer the primary yardsticks of value.

…

The dirty, dull, dangerous and demeaning jobs will be the first to be automated, and these remain, for better or worse, precisely the reservoir of opportunity for unskilled, undocumented or otherwise marginalized participants in the workforce. What this does to the culture of work, and to labor’s already imperiled ability to make demands and specify the conditions under which it produces value, deserves treatment at book length. For the present purposes, it seems safe to conclude that between algorithmic management and regulation, and the more than usually exploitative relations that we can see resulting from it,47 hard times are coming for those who have nothing to offer the economy but their muscle, their heart or their sex. I don’t doubt that those who benefit from any such state of affairs will be able to focus the rage of the permanently disemployed on immigrants and other convenient scapegoats, but eventually they too will be compelled to seek some sort of modus vivendi.

…

If we’re disturbed by what we find when we do finally answer queries like these to our own satisfaction, if we dislike the picture of the world and our place in it that we’re left with, we find ourselves confronted with a final set of questions: Can these radical technologies be renounced? Can they be resisted? And if not, can they at least be steered toward somewhat more congenial ends? The possibility of renunciation is easily enough dispensed with. Short of a determined, Kaczynskian flight from the consensual world and all its entanglements, the algorithmic management of life chances in particular will still exert tremendous pressure on the shape of one’s choices, even the structure of one’s consciousness. And even then—whatever steps they may have taken to secrete the traces of their existence from the network’s gaze, however blissfully unaware they may remain of its continuing interest in them—the dweller in a remote, off-the-grid cabin can be certain that data concerning them and their activities will continue to circulate indefinitely, turning up in response to queries and being operated on in unpredictable ways.

Hired: Six Months Undercover in Low-Wage Britain by James Bloodworth

Airbnb, algorithmic management, Berlin Wall, call centre, clockwatching, collective bargaining, congestion charging, credit crunch, deindustrialization, Fall of the Berlin Wall, fulfillment center, gentrification, gig economy, Greyball, independent contractor, Jeff Bezos, low skilled workers, Network effects, new economy, North Sea oil, Panopticon Jeremy Bentham, payday loans, post-truth, post-work, profit motive, race to the bottom, reshoring, scientific management, Silicon Valley, Travis Kalanick, Uber for X, working poor, working-age population

Aberfan disaster (1966) 170–1 ACAS 38 acid attacks, delivery drivers protest against, London (July, 2017) 256–7 Ackroyd, Peter 249 Admiral Insurance call centre, Swansea 150, 153–64, 180–1, 183, 185–6, 224 commission used as incentive for employees at 162–3 ‘fun’ culture 155, 161–2, 163, 164, 181 management 162–3, 224 performance league tables 183 politics, employee attitudes towards 164 ‘Renewals Consultant’ role 154 share scheme and dividends 159 staff turnover rate 159 training 155, 160–1 unions/collective action and 185, 186 university graduates employed at 153–4 wages/pay 155–6, 158–60, 164, 180 working hours and conditions 155, 160–4, 180–1, 185–6 Age UK 113 Aiden (building site worker) 135–6 Aiden (former miner) 175 Airbnb 217 Alex (former pit mechanic) 55, 57, 62–3 algorithmic management systems 16–17, 209, 210, 211, 217–18, 222, 223, 227, 231, 232, 242, 249 Aman (Uber driver) 236–8, 239–40, 241, 242, 255 Amazon: accommodation, employee 20–2, 24–6 algorithmic management system 16–17 blue badges 20, 41 breaks, employee 12–14, 36, 48, 49–50, 52–3, 64–5 British workers and 31, 33–4, 35–41, 57, 65, 72–3 diet/health of employees 51–2, 64–5, 70–1 disciplinary system 36, 39–41, 42–4 employment agencies, use of 19, 20, 37, 38, 39, 40, 41, 43, 65–6, 86 see also Transline and PMP Recruitment employment contracts 19–20, 53, 58 food served to employees 12–13, 14, 64 fulfillment centres in former mining areas 54–5 JB’s weekly budget whilst employed at 68–9 migrant labour, use of 11, 12, 13, 15, 20, 21, 22–7, 30, 32, 33, 34, 44, 45, 46, 51, 53, 57, 61–2, 65, 71–5, 258, 260–1 picker role 14, 16, 18, 19, 49, 65, 119, 258 process guide role 22–3 recruitment process 19–20 Rugeley distribution centre, Staffordshire 11–76, 79, 86, 119, 127, 128, 159, 258 security/security guards 11–13, 47, 48–9, 52 survey of employees, GMB 36 Swansea, warehouse in 145–6, 194 tax paid in UK by 146 tiredness/exhaustion of employees 44, 50–1, 65 transgender employees, treatment of 40–1 wages/salary 18, 19, 37–9, 42–3, 65–6, 68, 69, 70, 159 Amodeo, Michael 223 Anne (pensioner in Cwm) 197–8 anti-depressant medication 188 Armitage Shanks 57 Arora brothers 124–5 Aslam, Yaseen 229–30, 250 Assured Shorthold Tenancy 96 Attlee, Clement 173 ‘austerity’ policies 1–2, 6, 108 B&M Bargains 124–5, 126–30 BBC 138, 157, 173, 236 Bentham, Jeremy 182, 194 Berlin Wall, fall of (1989) 263 Bertram, Jo 235, 250–1 Bevan, Aneurin 144, 149, 192–3, 247 Bezos, Jeff 18 Big Issue, The 122 Big Pit National Coal Museum, Blaenavon 167, 170 Blackpool, Lancashire 77–140, 169, 187 accommodation in 80, 124, 137–8 B&M Bargains warehouse in 124–5, 126–31 Bloomfield district 137 building site work in 135–6 Central Drive 81, 120, 132–3 Golden Mile 121–2 health of residents 137 home care work in 81–90, 106–20, 140 homelessness in 95–105 job centres in 133–5 suicide rates in 100 unemployment in 121–3, 138, 139–40 Blaenau Gwent, Wales 187, 188, 190 see also under individual area and place name Booth, William 205 Brereton Colliery, Staffordshire 55 Brian (former miner) 196 Bryn Colliery, Wales 196 Brynmill, Swansea, Wales 150–1 building site work 121, 124, 135–6 buy-to-let housing market 24 Cadman, Scott 244, 245–6, 247–9 call centres 35, 61, 139, 150, 153–64, 180–6, 192, 199, 224 see also Admiral Insurance call centre, Swansea Cameron, David 259 Cannock Chase 21, 28, 54 capitalism 83, 145, 181 co-opts rebellion against 149 consumerism and 146 debt, reliance on 62 English culture overwhelmed by 32–3, 198–9 fall of Berlin Wall (1989) and 263 ‘gig’ economy and 210, 215, 232 platform capitalism 215 religious fatalism appropriated by 161 care sector: Eastern European migrant labour and 114–15 length of home care visits and 108–9, 110 local authority budget cuts and 107–10 privatisation of social care and 106–8, 109 staff training in 85–6 staffing crisis within 84–5, 119 zero hours contracts and 87 see also home care worker Carewatch UK 81–90, 109, 110, 118, 132, 135, 136, 150, 159 Disclosure and Barring Service (DBS) process and 88–90, 109–10 employee reviews of 83–4 employment contracts/conditions 87–8, 118–19 length of care visits and 110 MAR (Medication Administration Record) sheets and 114, 115 recruitment 81–2, 84–5 ‘shadowing’ process 88, 109–10 training 85–6 see also care sector and home care worker Cefn Mawr No. 2, Afan Valley, Wales 171–2 Celcon 57 Centre for Cities 61 Chartered Institute of Personnel and Development 153 Chartists 144, 149 China 183, 196–7 Chris (Amazon employee) 20, 21, 22–6, 65 Citizens Advice 243–4 CitySprint 246, 248–9, 251–2 Claire (Amazon employee) 36, 37–41, 50, 53 class: death of 4 erosion of class solidarity 193–4 fall of Berlin Wall and 263 liberalism and 263 scientific theories of 4, 17 see also middle-class and working-class Claudiu (housemate of JB) 22 coalition government (2010–15) 109, 115–16 coal mining: decline of industry 54, 55–6, 58, 144–5, 172–9 danger of/disasters 169–72 General Strike and 173 Miners’ Strike (1984–5) 3, 174–7 South Wales Valleys and 143–4, 147–9, 165–79, 180, 188, 189, 190–1, 193, 195, 196 Thatcher and 174–5, 263–4 collectivism 228 communism 17, 173, 178, 228, 263 Compare the Market 155 Conservative Party 3, 7, 109, 175 consumerism 146 Coombes, B.

Blood in the Machine: The Origins of the Rebellion Against Big Tech by Brian Merchant

"World Economic Forum" Davos, Ada Lovelace, algorithmic management, Amazon Mechanical Turk, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, basic income, Bernie Sanders, Big Tech, big-box store, Black Lives Matter, Cambridge Analytica, Charles Babbage, ChatGPT, collective bargaining, colonial rule, commoditize, company town, computer age, computer vision, coronavirus, cotton gin, COVID-19, cryptocurrency, DALL-E, decarbonisation, deskilling, digital rights, Donald Trump, Edward Jenner, Elon Musk, Erik Brynjolfsson, factory automation, flying shuttle, Frederick Winslow Taylor, fulfillment center, full employment, future of work, George Floyd, gig economy, gigafactory, hiring and firing, hockey-stick growth, independent contractor, industrial robot, information asymmetry, Internet Archive, invisible hand, Isaac Newton, James Hargreaves, James Watt: steam engine, Jeff Bezos, Jessica Bruder, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Roose, Kickstarter, Lyft, Mark Zuckerberg, Marshall McLuhan, means of production, military-industrial complex, move fast and break things, Naomi Klein, New Journalism, On the Economy of Machinery and Manufactures, OpenAI, precariat, profit motive, ride hailing / ride sharing, Sam Bankman-Fried, scientific management, Second Machine Age, self-driving car, sharing economy, Silicon Valley, sovereign wealth fund, spinning jenny, Steve Jobs, Steve Wozniak, super pumped, TaskRabbit, tech billionaire, tech bro, tech worker, techlash, technological determinism, Ted Kaczynski, The Future of Employment, The Wealth of Nations by Adam Smith, Thomas Malthus, Travis Kalanick, Uber and Lyft, uber lyft, union organizing, universal basic income, W. E. B. Du Bois, warehouse automation, warehouse robotics, working poor, workplace surveillance

Maybe you don’t even need to imagine this, because you live in the twenty-first century and have seen a corporation, a platform, or a manager use technology to rewrite the social contract that once defined your own job. Maybe you are a cab driver who saved up for years to own your own car and medallion, who knows miles of city streets as if they were your own backyard, only to have Uber show up on the scene, undercut prices with its store of venture capital and its algorithmic management system, and render your investment worthless. Or maybe you have worked for years as a salesperson, acquiring contacts and relationships with vendors, only to see your company introduce an automated portal that performs most of your role. Or maybe you are a writer or an artist who was let go from the digital media company that published your work, just as the outlet announced it would begin using AI to generate content.

…

The algorithm-based gig work model is the next stage in the evolution of the factory, a mode of control over workers that extends beyond mass production and is superior in nearly every way. This explains why Amazon, the second-largest employer in the United States, has adopted such a model, with its Uber-like Flex program for delivery drivers and fully automated hiring and HR systems. Gig app platforms and algorithmic management seek to reduce or eliminate the need for middle managers or HR departments, at least for the working-class “independent contractors” who constitute the bulk of a company’s labor force. Imperturbable algorithms provide the final say on where workers will go and how much they will make. There is little need for office space for the laborers; workers are distributed and diffuse.

…

“There is no doubt that companies across the tech and service economy are trying to move to a 1099 workforce”—one that runs on precarious contract labor rather than salaried jobs—“which dramatically lowers labor costs and transfers risks and liabilities,” Dubal says. That has grown to brand new heights. We’ll see it more in restaurant work and retail and education and health and hospitality for sure. In addition to taking rights and security away from people who really need them, these business practices are infused with algorithmic management practices. The law hinges on the amount of control the employer exerts on the employee—so much of the control is exerted through social psychological gaming algorithms. It looks like you have choice and volition, but really it’s training you to work for the platform in a specific way. There is a purposefully generated information asymmetry, Dubal says.

New Power: How Power Works in Our Hyperconnected World--And How to Make It Work for You by Jeremy Heimans, Henry Timms

"Susan Fowler" uber, "World Economic Forum" Davos, 3D printing, 4chan, Affordable Care Act / Obamacare, Airbnb, algorithmic management, augmented reality, autonomous vehicles, battle of ideas, benefit corporation, Benjamin Mako Hill, Big Tech, bitcoin, Black Lives Matter, blockchain, British Empire, Chris Wanstrath, Columbine, Corn Laws, crowdsourcing, data science, David Attenborough, death from overwork, Donald Trump, driverless car, Elon Musk, fake news, Ferguson, Missouri, future of work, game design, gig economy, hiring and firing, holacracy, hustle culture, IKEA effect, impact investing, income inequality, informal economy, job satisfaction, John Zimmer (Lyft cofounder), Jony Ive, Kevin Roose, Kibera, Kickstarter, Lean Startup, Lyft, Mark Zuckerberg, Minecraft, Network effects, new economy, Nicholas Carr, obamacare, Occupy movement, post-truth, profit motive, race to the bottom, radical decentralization, ride hailing / ride sharing, rolling blackouts, rolodex, Salesforce, Saturday Night Live, sharing economy, side hustle, Silicon Valley, six sigma, Snapchat, social web, subscription business, TaskRabbit, tech billionaire, TED Talk, the scientific method, transaction costs, Travis Kalanick, Uber and Lyft, uber lyft, upwardly mobile, web application, WikiLeaks, Yochai Benkler

Consider a platform like TaskRabbit, for which the vast majority of workers are contingent. Such businesses rely on an initial layer of what we can think of as algorithmic management; the very design of their platforms allows them to enforce rules and create incentives for desired behavior. Customer rating systems stand in for performance reviews. Allowing workers to rate customers can maintain morale by weeding out the bad apples; workers in traditional service roles rarely get to impose real consequences on a mean or haranguing customer. But algorithmic management alone will only go so far. Creating a human connection among workers on a vast scale will also be critical.

Exponential: How Accelerating Technology Is Leaving Us Behind and What to Do About It by Azeem Azhar

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 23andMe, 3D printing, A Declaration of the Independence of Cyberspace, Ada Lovelace, additive manufacturing, air traffic controllers' union, Airbnb, algorithmic management, algorithmic trading, Amazon Mechanical Turk, autonomous vehicles, basic income, Berlin Wall, Bernie Sanders, Big Tech, Bletchley Park, Blitzscaling, Boeing 737 MAX, book value, Boris Johnson, Bretton Woods, carbon footprint, Chris Urmson, Citizen Lab, Clayton Christensen, cloud computing, collective bargaining, computer age, computer vision, contact tracing, contact tracing app, coronavirus, COVID-19, creative destruction, crowdsourcing, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, David Graeber, David Ricardo: comparative advantage, decarbonisation, deep learning, deglobalization, deindustrialization, dematerialisation, Demis Hassabis, Diane Coyle, digital map, digital rights, disinformation, Dissolution of the Soviet Union, Donald Trump, Double Irish / Dutch Sandwich, drone strike, Elon Musk, emotional labour, energy security, Fairchild Semiconductor, fake news, Fall of the Berlin Wall, Firefox, Frederick Winslow Taylor, fulfillment center, future of work, Garrett Hardin, gender pay gap, general purpose technology, Geoffrey Hinton, gig economy, global macro, global pandemic, global supply chain, global value chain, global village, GPT-3, Hans Moravec, happiness index / gross national happiness, hiring and firing, hockey-stick growth, ImageNet competition, income inequality, independent contractor, industrial robot, intangible asset, Jane Jacobs, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, Just-in-time delivery, Kickstarter, Kiva Systems, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, lockdown, low skilled workers, lump of labour, Lyft, manufacturing employment, Marc Benioff, Mark Zuckerberg, megacity, Mitch Kapor, Mustafa Suleyman, Network effects, new economy, NSO Group, Ocado, offshore financial centre, OpenAI, PalmPilot, Panopticon Jeremy Bentham, Peter Thiel, Planet Labs, price anchoring, RAND corporation, ransomware, Ray Kurzweil, remote working, RFC: Request For Comment, Richard Florida, ride hailing / ride sharing, Robert Bork, Ronald Coase, Ronald Reagan, Salesforce, Sam Altman, scientific management, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, software as a service, Steve Ballmer, Steve Jobs, Stuxnet, subscription business, synthetic biology, tacit knowledge, TaskRabbit, tech worker, The Death and Life of Great American Cities, The Future of Employment, The Nature of the Firm, Thomas Malthus, TikTok, Tragedy of the Commons, Turing machine, Uber and Lyft, Uber for X, uber lyft, universal basic income, uranium enrichment, vertical integration, warehouse automation, winner-take-all economy, workplace surveillance , Yom Kippur War

Workers on platform apps have diminishing control over how they work. When dozens of gig workers for Uber Eats gathered outside the company’s office in south London in 2016 to protest, they were not only criticising their low pay. They were questioning the core of what makes the gig economy giants, especially ride-hailing companies, successful: algorithmic management. ‘We are people, not Uber’s tools,’ they yelled. These people and millions of other gig workers are managed by computers. Their work is scrutinised through a stream of quantitative performance assessments. Rideshare drivers may only have 10–20 seconds to respond to an offered ride, without knowing in advance where they’re expected to go or how much they can expect to make.

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Without them, workers may well find themselves unable to keep up with the clip of the Exponential Age. The great risk facing workers in the Exponential Age doesn’t come from robots. It comes from a rapidly changing economy – defined by a fundamental shift in the quality of working arrangements, resulting from gig-working and algorithmic management. For workers, this leads to an age-old problem: a power imbalance between bosses and workers. But while this imbalance is a consequence of the Exponential Age, it is not an inevitable one. 6 The World Is Spiky Angelo Yu had a problem. It was late 2019, and US president Donald Trump had spent much of the previous two years tweeting increasingly bellicose denunciations of the Chinese government.

Virtual Competition by Ariel Ezrachi, Maurice E. Stucke

"World Economic Forum" Davos, Airbnb, Alan Greenspan, Albert Einstein, algorithmic management, algorithmic trading, Arthur D. Levinson, barriers to entry, behavioural economics, cloud computing, collaborative economy, commoditize, confounding variable, corporate governance, crony capitalism, crowdsourcing, Daniel Kahneman / Amos Tversky, David Graeber, deep learning, demand response, Didi Chuxing, digital capitalism, disintermediation, disruptive innovation, double helix, Downton Abbey, driverless car, electricity market, Erik Brynjolfsson, Evgeny Morozov, experimental economics, Firefox, framing effect, Google Chrome, independent contractor, index arbitrage, information asymmetry, interest rate derivative, Internet of things, invisible hand, Jean Tirole, John Markoff, Joseph Schumpeter, Kenneth Arrow, light touch regulation, linked data, loss aversion, Lyft, Mark Zuckerberg, market clearing, market friction, Milgram experiment, multi-sided market, natural language processing, Network effects, new economy, nowcasting, offshore financial centre, pattern recognition, power law, prediction markets, price discrimination, price elasticity of demand, price stability, profit maximization, profit motive, race to the bottom, rent-seeking, Richard Thaler, ride hailing / ride sharing, road to serfdom, Robert Bork, Ronald Reagan, search costs, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, smart meter, Snapchat, social graph, Steve Jobs, sunk-cost fallacy, supply-chain management, telemarketer, The Chicago School, The Myth of the Rational Market, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, Travis Kalanick, turn-by-turn navigation, two-sided market, Uber and Lyft, Uber for X, uber lyft, vertical integration, Watson beat the top human players on Jeopardy!, women in the workforce, yield management

Min Kyung Lee, Daniel Kusbit, Evan Metsky, and Laura Dabbish, “Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers” (Pittsburgh: Human-Computer Interaction Institute, Heinz College, Carnegie Mellon University, 2015), http://www.cs.cmu.edu /~mklee/materials/Publication/2015-CHI _ algorithmic _management.pdf. For instance, in Tesco v. Office of Fair Trading, the U.K. Competition Appeal Tribunal elaborated that an indirect information exchange through a third party will amount to an objectionable hub-and-spoke conspiracy when two phases are present: 1. Retailer A discloses to supplier B its future pricing, with the intention that B will pass that information to other retailers in order to influence market conditions. 2.

…

Min Kyung Lee, Daniel Kusbit, Evan Metsky, and Laura Dabbish, “Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers,” Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (New York: ACM, 2015), http://www .cs.cmu.edu/~mklee/materials/Publication/2015-CHI_ algorithmic _management.pdf. Ibid. Uber, Interested in Driving with Uber? https://get.uber.com/drive/. John Kenneth Galbraith, The Essential Galbraith (Boston: Mariner Books, 2010), 72. Ibid. Eden Medina, Cybernetic Revolutionaries: Technology and Politics in Allende’s Chile (Cambridge, MA: MIT Press, 2011). Evgeny Morozov, “The Planning Machine: Project Cybersyn and the Origins of the Big Data Nation,” New Yorker, October 13, 2014, http://www.newyorker .com/magazine/2014/10/13/planning-machine.

Design of Business: Why Design Thinking Is the Next Competitive Advantage by Roger L. Martin

algorithmic management, Apple Newton, asset allocation, autism spectrum disorder, Buckminster Fuller, business process, Frank Gehry, global supply chain, high net worth, Innovator's Dilemma, Isaac Newton, mobile money, planned obsolescence, QWERTY keyboard, Ralph Waldo Emerson, risk tolerance, Salesforce, scientific management, six sigma, Steve Ballmer, Steve Jobs, stock buybacks, supply-chain management, Wall-E, winner-take-all economy

Rewards and high status flow to those managers who analyze past performance to refine heuristics and algorithms, and the highest status and biggest rewards accrue to the executive who reliably runs the most important heuristic or algorithm, importance being measured by revenue and profit. Think of Goldman Sachs’s sales and trading heuristic or McDonald’s U.S. business algorithm. Managers do their best to dodge tricky smaller businesses that face complicated mysteries, which are seen as detours to advancement, if not career dead ends. Counterproductive Pressure from the Public Capital Markets All too often, companies mismanage the resources freed up by movement along the knowledge funnel.

The Internet Is Not What You Think It Is: A History, a Philosophy, a Warning by Justin E. H. Smith

3D printing, Ada Lovelace, Adrian Hon, agricultural Revolution, algorithmic management, artificial general intelligence, Big Tech, Charles Babbage, clean water, coronavirus, COVID-19, cryptocurrency, dark matter, disinformation, Donald Trump, drone strike, Elon Musk, game design, gamification, global pandemic, GPT-3, Internet of things, Isaac Newton, Jacquard loom, Jacques de Vaucanson, Jaron Lanier, jimmy wales, Joseph-Marie Jacquard, Kuiper Belt, Mark Zuckerberg, Marshall McLuhan, meme stock, new economy, Nick Bostrom, Norbert Wiener, packet switching, passive income, Potemkin village, printed gun, QAnon, Ray Kurzweil, Republic of Letters, Silicon Valley, Skype, strong AI, technological determinism, theory of mind, TikTok, Tragedy of the Commons, trolley problem, Turing machine, Turing test, you are the product

We are not, yet, accustomed to seeing these different trends—the corporate opportunism of Ancestry and Spotify; the sinister right-wing populism of the aforementioned leaders; and the identitarian campaigns for cultural purity driven mostly by young self-styled “progressives” on social media—as inflections of the same broad historical phenomenon. But perhaps their commonality may become clearer when we consider all of them as symptoms of an underlying and much vaster historical shift: the shift to ubiquitous algorithmic management of society, which lends advantage to the expression of opinions unambigous enough (i.e., dogmatic or extremist enough) for AI to detect their meaning and to process them accordingly, and which also removes from the individual subject any deep existential imperative or moral duty to cultivate self-understanding, instead allowing the sort of vectors of identity that even AI can pick up and process to substitute for any real idea of who an individual is or might yet hope to be.

Human + Machine: Reimagining Work in the Age of AI by Paul R. Daugherty, H. James Wilson

3D printing, AI winter, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Amazon Robotics, augmented reality, autonomous vehicles, blockchain, business process, call centre, carbon footprint, circular economy, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, data science, deep learning, DeepMind, digital twin, disintermediation, Douglas Hofstadter, driverless car, en.wikipedia.org, Erik Brynjolfsson, fail fast, friendly AI, fulfillment center, future of work, Geoffrey Hinton, Hans Moravec, industrial robot, Internet of things, inventory management, iterative process, Jeff Bezos, job automation, job satisfaction, knowledge worker, Lyft, machine translation, Marc Benioff, natural language processing, Neal Stephenson, personalized medicine, precision agriculture, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, robotic process automation, Rodney Brooks, Salesforce, Second Machine Age, self-driving car, sensor fusion, sentiment analysis, Shoshana Zuboff, Silicon Valley, Snow Crash, software as a service, speech recognition, tacit knowledge, telepresence, telepresence robot, text mining, the scientific method, uber lyft, warehouse automation, warehouse robotics

The term is meant to call attention to the ways in which automated and autonomous systems deflect responsibility in unique, systematic ways. While the crumple zone in a car is meant to protect the human driver, the moral crumple zone protects the integrity of the technological system, itself.14 For algorithmically-managed crowd platforms, human operators can also become “liability sponges,” getting bad feedback from a customer when it’s really the system’s fault, for instance. Additionally, they bear the brunt of expenses on their cars—the insurance, the gas, the wear and tear, all the while absorbing the liability on behalf of the ride-hailing app if something goes wrong with their ride-giving vehicle.

The Currency Cold War: Cash and Cryptography, Hash Rates and Hegemony by David G. W. Birch

"World Economic Forum" Davos, Alan Greenspan, algorithmic management, AlphaGo, bank run, Big Tech, bitcoin, blockchain, Bretton Woods, BRICs, British Empire, business cycle, capital controls, cashless society, central bank independence, COVID-19, cross-border payments, cryptocurrency, Diane Coyle, disintermediation, distributed ledger, Donald Trump, driverless car, Elon Musk, Ethereum, ethereum blockchain, facts on the ground, fault tolerance, fiat currency, financial exclusion, financial innovation, financial intermediation, floating exchange rates, forward guidance, Fractional reserve banking, global reserve currency, global supply chain, global village, Hyman Minsky, information security, initial coin offering, Internet of things, Jaron Lanier, Kenneth Rogoff, knowledge economy, M-Pesa, Mark Zuckerberg, market clearing, market design, Marshall McLuhan, mobile money, Money creation, money: store of value / unit of account / medium of exchange, moral hazard, Network effects, new economy, Northern Rock, one-China policy, Overton Window, PalmPilot, pattern recognition, Pingit, QR code, quantum cryptography, race to the bottom, railway mania, ransomware, Real Time Gross Settlement, reserve currency, Satoshi Nakamoto, seigniorage, Silicon Valley, smart contracts, social distancing, sovereign wealth fund, special drawing rights, subscription business, the payments system, too big to fail, transaction costs, Vitalik Buterin, Washington Consensus

Maintaining stability Using this breakdown, and assuming that partially collateralized currencies will struggle to gain confidence and that collateralization by crypto is an unproven (and potentially dangerous) model, I think we should focus on three main mechanisms to deliver a stable currency. Algorithms, in which algorithms manage supply and demand to obtain stability of the digital currency. This is what a real stable cryptocurrency would be. Since a cryptocurrency is backed by nothing other than mathematics, it is mathematics that manages the money supply to hold the value steady against some external benchmark.27 Assets, in which an asset or basket of assets is used to back the digital currency.

Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future by Luke Dormehl

"World Economic Forum" Davos, Ada Lovelace, agricultural Revolution, AI winter, Albert Einstein, Alexey Pajitnov wrote Tetris, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Apple II, artificial general intelligence, Automated Insights, autonomous vehicles, backpropagation, Bletchley Park, book scanning, borderless world, call centre, cellular automata, Charles Babbage, Claude Shannon: information theory, cloud computing, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, deep learning, DeepMind, driverless car, drone strike, Elon Musk, Flash crash, Ford Model T, friendly AI, game design, Geoffrey Hinton, global village, Google X / Alphabet X, Hans Moravec, hive mind, industrial robot, information retrieval, Internet of things, iterative process, Jaron Lanier, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, life extension, Loebner Prize, machine translation, Marc Andreessen, Mark Zuckerberg, Menlo Park, Mustafa Suleyman, natural language processing, Nick Bostrom, Norbert Wiener, out of africa, PageRank, paperclip maximiser, pattern recognition, radical life extension, Ray Kurzweil, recommendation engine, remote working, RFID, scientific management, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, social intelligence, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, tech billionaire, technological singularity, The Coming Technological Singularity, The Future of Employment, Tim Cook: Apple, Tony Fadell, too big to fail, traumatic brain injury, Turing machine, Turing test, Vernor Vinge, warehouse robotics, Watson beat the top human players on Jeopardy!

Letting the computer analyse the songs revealed 2,883 unique numerical content descriptors, noting everything from pitch and tempo to other patterns we don’t commonly associate with music. Shamir then used a statistical tool called the K-Nearest Neighbor algorithm to determine the measure of similarity between any two songs in the database. Without human intervention, the algorithm managed to sort all thirteen albums into chronological order, beginning with 1963’s Please Please Me, before proceeding to With the Beatles, A Hard Day’s Night, Beatles for Sale, Help!, Rubber Soul, Revolver, Sergeant Pepper’s Lonely Hearts Club Band, Magical Mystery Tour, The White Album, Yellow Submarine, Let It Be and – finally – Abbey Road.

Hello World: Being Human in the Age of Algorithms by Hannah Fry

23andMe, 3D printing, Air France Flight 447, Airbnb, airport security, algorithmic bias, algorithmic management, augmented reality, autonomous vehicles, backpropagation, Brixton riot, Cambridge Analytica, chief data officer, computer vision, crowdsourcing, DARPA: Urban Challenge, data science, deep learning, DeepMind, Douglas Hofstadter, driverless car, Elon Musk, fake news, Firefox, Geoffrey Hinton, Google Chrome, Gödel, Escher, Bach, Ignaz Semmelweis: hand washing, John Markoff, Mark Zuckerberg, meta-analysis, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, pattern recognition, Peter Thiel, RAND corporation, ransomware, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, Shai Danziger, Silicon Valley, Silicon Valley startup, Snapchat, sparse data, speech recognition, Stanislav Petrov, statistical model, Stephen Hawking, Steven Levy, systematic bias, TED Talk, Tesla Model S, The Wisdom of Crowds, Thomas Bayes, trolley problem, Watson beat the top human players on Jeopardy!, web of trust, William Langewiesche, you are the product

They have a website, open to anyone, where you can try it for yourself. Since my Twitter profile is open to the public anyway, I thought I’d try out the researchers’ predictions myself, so uploaded my Twitter history and filled out a traditional questionnaire-based personality study to compare. The algorithm managed to assess me accurately on three of the five traits. Although, as it turns out, according to the traditional personality study I am much more extraverted and neurotic than my Twitter profile makes it seem.‡ All this work was motivated by how it could be used in advertising. So, by 2017,21 the same team of academics had moved on to experimenting with sending out adverts tailored to an individual’s personality traits.

The End of Absence: Reclaiming What We've Lost in a World of Constant Connection by Michael Harris

4chan, Albert Einstein, algorithmic management, AltaVista, Andrew Keen, augmented reality, Burning Man, Carrington event, cognitive dissonance, crowdsourcing, dematerialisation, disinformation, en.wikipedia.org, Evgeny Morozov, Filter Bubble, Firefox, Google Glasses, informal economy, information retrieval, invention of movable type, invention of the printing press, invisible hand, James Watt: steam engine, Jaron Lanier, jimmy wales, Kevin Kelly, Lewis Mumford, lifelogging, Loebner Prize, low earth orbit, Marshall McLuhan, McMansion, moral panic, Nicholas Carr, off-the-grid, pattern recognition, Plato's cave, pre–internet, Republic of Letters, Silicon Valley, Skype, Snapchat, social web, Steve Jobs, technological solutionism, TED Talk, the medium is the message, The Wisdom of Crowds, traumatic brain injury, Turing test

Nor do we consider to what authority we’re doing the confessing. This is because the means of confession—the technology itself—is so very amiable. Dinakar is building a more welcoming online world, and it’s a good thing he is. But we need to remain critical as we give over so much of ourselves to algorithmic management. • • • • • In a sense, Dinakar and others at the Media Lab are still pursuing Alan Turing’s dream. “I want to compute for empathy,” Dinakar told me as our time together wound down. “I don’t want to compute for banning anyone. I just want . . . I want the world to be a less lonely place.”

Cogs and Monsters: What Economics Is, and What It Should Be by Diane Coyle

3D printing, additive manufacturing, Airbnb, Al Roth, Alan Greenspan, algorithmic management, Amazon Web Services, autonomous vehicles, banking crisis, barriers to entry, behavioural economics, Big bang: deregulation of the City of London, biodiversity loss, bitcoin, Black Lives Matter, Boston Dynamics, Bretton Woods, Brexit referendum, business cycle, call centre, Carmen Reinhart, central bank independence, choice architecture, Chuck Templeton: OpenTable:, cloud computing, complexity theory, computer age, conceptual framework, congestion charging, constrained optimization, coronavirus, COVID-19, creative destruction, credit crunch, data science, DeepMind, deglobalization, deindustrialization, Diane Coyle, discounted cash flows, disintermediation, Donald Trump, Edward Glaeser, en.wikipedia.org, endogenous growth, endowment effect, Erik Brynjolfsson, eurozone crisis, everywhere but in the productivity statistics, Evgeny Morozov, experimental subject, financial deregulation, financial innovation, financial intermediation, Flash crash, framing effect, general purpose technology, George Akerlof, global supply chain, Goodhart's law, Google bus, haute cuisine, High speed trading, hockey-stick growth, Ida Tarbell, information asymmetry, intangible asset, Internet of things, invisible hand, Jaron Lanier, Jean Tirole, job automation, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, knowledge economy, knowledge worker, Les Trente Glorieuses, libertarian paternalism, linear programming, lockdown, Long Term Capital Management, loss aversion, low earth orbit, lump of labour, machine readable, market bubble, market design, Menlo Park, millennium bug, Modern Monetary Theory, Mont Pelerin Society, multi-sided market, Myron Scholes, Nash equilibrium, Nate Silver, Network effects, Occupy movement, Pareto efficiency, payday loans, payment for order flow, Phillips curve, post-industrial society, price mechanism, Productivity paradox, quantitative easing, randomized controlled trial, rent control, rent-seeking, ride hailing / ride sharing, road to serfdom, Robert Gordon, Robert Shiller, Robert Solow, Robinhood: mobile stock trading app, Ronald Coase, Ronald Reagan, San Francisco homelessness, savings glut, school vouchers, sharing economy, Silicon Valley, software is eating the world, spectrum auction, statistical model, Steven Pinker, tacit knowledge, The Chicago School, The Future of Employment, The Great Moderation, the map is not the territory, The Rise and Fall of American Growth, the scientific method, The Signal and the Noise by Nate Silver, the strength of weak ties, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Uber for X, urban planning, winner-take-all economy, Winter of Discontent, women in the workforce, Y2K

As described in Chapter Five, high fixed costs of starting up and network effects in digital business mean there are often these very large increasing returns to scale. As Shalizi observes: ‘[T]here are no general-purpose algorithms for optimizing under non-convex constraints. Non-convex programming isn’t roughly as tractable as linear programming, it’s generally quite intractable.’ This may be overstating the impossibility, as algorithms manage to address similar problems such as how should a logistics firm collect and deliver millions of parcels across the world; but at the scale of the whole economy with all the varieties it contains, it is certainly challenging. And these non-convex or increasing returns phenomena are pervasive in the modern, service- and knowledge-based digital economy.

What Algorithms Want: Imagination in the Age of Computing by Ed Finn

Airbnb, Albert Einstein, algorithmic bias, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, bitcoin, blockchain, business logic, Charles Babbage, Chuck Templeton: OpenTable:, Claude Shannon: information theory, commoditize, Computing Machinery and Intelligence, Credit Default Swap, crowdsourcing, cryptocurrency, data science, DeepMind, disruptive innovation, Donald Knuth, Donald Shoup, Douglas Engelbart, Douglas Engelbart, Elon Musk, Evgeny Morozov, factory automation, fiat currency, Filter Bubble, Flash crash, game design, gamification, Google Glasses, Google X / Alphabet X, Hacker Conference 1984, High speed trading, hiring and firing, Ian Bogost, industrial research laboratory, invisible hand, Isaac Newton, iterative process, Jaron Lanier, Jeff Bezos, job automation, John Conway, John Markoff, Just-in-time delivery, Kickstarter, Kiva Systems, late fees, lifelogging, Loebner Prize, lolcat, Lyft, machine readable, Mother of all demos, Nate Silver, natural language processing, Neal Stephenson, Netflix Prize, new economy, Nicholas Carr, Nick Bostrom, Norbert Wiener, PageRank, peer-to-peer, Peter Thiel, power law, Ray Kurzweil, recommendation engine, Republic of Letters, ride hailing / ride sharing, Satoshi Nakamoto, self-driving car, sharing economy, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Silicon Valley startup, SimCity, Skinner box, Snow Crash, social graph, software studies, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, supply-chain management, tacit knowledge, TaskRabbit, technological singularity, technological solutionism, technoutopianism, the Cathedral and the Bazaar, The Coming Technological Singularity, the scientific method, The Signal and the Noise by Nate Silver, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, transaction costs, traveling salesman, Turing machine, Turing test, Uber and Lyft, Uber for X, uber lyft, urban planning, Vannevar Bush, Vernor Vinge, wage slave

Uber is merely one prominent example of the broader movement to build this interface layer into many different cultural spaces, from hiring contractors for home repair to facilitating private party car sales. All of these markets were, of course, already technological, but they were largely inaccessible to direct algorithmic management until the advent of smartphones and ubiquitous sensors enabling the close monitoring of human and financial resources. In terms of labor and surplus value, what the algorithms of Uber, Airbnb, and their cohort capitalize on is the slack infrastructure of modern consumption: empty cars, unused bedrooms, and under-employed people.

The Meritocracy Trap: How America's Foundational Myth Feeds Inequality, Dismantles the Middle Class, and Devours the Elite by Daniel Markovits

8-hour work day, activist fund / activist shareholder / activist investor, affirmative action, algorithmic management, Amazon Robotics, Anton Chekhov, asset-backed security, assortative mating, basic income, Bernie Sanders, big-box store, business cycle, capital asset pricing model, Capital in the Twenty-First Century by Thomas Piketty, Carl Icahn, carried interest, collateralized debt obligation, collective bargaining, compensation consultant, computer age, corporate governance, corporate raider, crony capitalism, David Brooks, deskilling, Detroit bankruptcy, disruptive innovation, Donald Trump, Edward Glaeser, Emanuel Derman, equity premium, European colonialism, everywhere but in the productivity statistics, fear of failure, financial engineering, financial innovation, financial intermediation, fixed income, Ford paid five dollars a day, Frederick Winslow Taylor, fulfillment center, full employment, future of work, gender pay gap, gentrification, George Akerlof, Gini coefficient, glass ceiling, Glass-Steagall Act, Greenspan put, helicopter parent, Herbert Marcuse, high net worth, hiring and firing, income inequality, industrial robot, interchangeable parts, invention of agriculture, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, junk bonds, Kevin Roose, Kiva Systems, knowledge economy, knowledge worker, Kodak vs Instagram, labor-force participation, Larry Ellison, longitudinal study, low interest rates, low skilled workers, machine readable, manufacturing employment, Mark Zuckerberg, Martin Wolf, mass incarceration, medical residency, meritocracy, minimum wage unemployment, Myron Scholes, Nate Silver, New Economic Geography, new economy, offshore financial centre, opioid epidemic / opioid crisis, Paul Samuelson, payday loans, plutocrats, Plutonomy: Buying Luxury, Explaining Global Imbalances, precariat, purchasing power parity, rent-seeking, Richard Florida, Robert Gordon, Robert Shiller, Robert Solow, Ronald Reagan, Rutger Bregman, savings glut, school choice, shareholder value, Silicon Valley, Simon Kuznets, six sigma, Skype, stakhanovite, stem cell, Stephen Fry, Steve Jobs, stock buybacks, supply-chain management, telemarketer, The Bell Curve by Richard Herrnstein and Charles Murray, The Theory of the Leisure Class by Thorstein Veblen, Thomas Davenport, Thorstein Veblen, too big to fail, total factor productivity, transaction costs, traveling salesman, universal basic income, unpaid internship, Vanguard fund, War on Poverty, warehouse robotics, Winter of Discontent, women in the workforce, work culture , working poor, Yochai Benkler, young professional, zero-sum game

Across restructurings in the 1980s and 1990s, middle managers were downsized at nearly twice the rate of nonmanagerial workers. And the share of all managers aged forty-five to sixty-four whose job tenure exceeded fifteen years has collapsed (falling by more than a quarter in just the two decades between 1987 and 2006). The process, moreover, continues today. Algorithmic management consulting firms now expressly seek “not [to] automat[e] [line workers’] jobs per se, but [rather to] automat[e] the [middle] manager’s job.” All this downsizing is driven by structural considerations rather than by firm-specific economic distress: it hits profitable as well as unprofitable firms, continues during economic booms as well as busts, and peaked during the epochal economic boom in the 1990s.

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never met middle management: Min Kying Lee et al., “Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers,” Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (April 2015): 1603, www.cs.cmu.edu/~mklee/materials/Publication/2015-CHI_algorithmic_management.pdf. every assembly line: For a broad overview of modern supply chain management, see generally Martin Christopher, Logistics and Supply Chain Management, 5th ed. (Harlow: Pearson, 2016), 35 (discussing how “just-in-time” strategy results in minimal inventory), 194 (the use of event management software to manage inventory levels), 225–26 (discussing the merits of Six Sigma management techniques), 289 (a change-embracing corporate culture).

Data and the City by Rob Kitchin,Tracey P. Lauriault,Gavin McArdle

A Declaration of the Independence of Cyberspace, algorithmic management, bike sharing, bitcoin, blockchain, Bretton Woods, Chelsea Manning, citizen journalism, Claude Shannon: information theory, clean water, cloud computing, complexity theory, conceptual framework, corporate governance, correlation does not imply causation, create, read, update, delete, crowdsourcing, cryptocurrency, data science, dematerialisation, digital divide, digital map, digital rights, distributed ledger, Evgeny Morozov, fault tolerance, fiat currency, Filter Bubble, floating exchange rates, folksonomy, functional programming, global value chain, Google Earth, Hacker News, hive mind, information security, Internet of things, Kickstarter, knowledge economy, Lewis Mumford, lifelogging, linked data, loose coupling, machine readable, new economy, New Urbanism, Nicholas Carr, nowcasting, open economy, openstreetmap, OSI model, packet switching, pattern recognition, performance metric, place-making, power law, quantum entanglement, RAND corporation, RFID, Richard Florida, ride hailing / ride sharing, semantic web, sentiment analysis, sharing economy, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, smart grid, smart meter, social graph, software studies, statistical model, tacit knowledge, TaskRabbit, technological determinism, technological solutionism, text mining, The Chicago School, The Death and Life of Great American Cities, the long tail, the market place, the medium is the message, the scientific method, Toyota Production System, urban planning, urban sprawl, web application

However, to date, there has been relatively little critical reflection on the new emerging relationship between data and the city, and how we come to know and understand cities through data in the present era. In the rush to create so-called ‘smart cities’, wherein core city services and infrastructures become digitally mediated and data-driven – generating, processing and acting on data in real-time to algorithmically manage systems and calibrate performance – much of the attention has been on how to technically create and implement suitable smart city technologies, and associated institutional and infrastructural supports such as data standards, protocols, policies, and a variety of telecom networks. Such data-driven technologies include: urban control rooms, e-government systems, city operating systems, coordinated emergency 2 R.

Rise of the Robots: Technology and the Threat of a Jobless Future by Martin Ford

3D printing, additive manufacturing, Affordable Care Act / Obamacare, AI winter, algorithmic management, algorithmic trading, Amazon Mechanical Turk, artificial general intelligence, assortative mating, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Bernie Madoff, Bill Joy: nanobots, bond market vigilante , business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, Charles Babbage, Chris Urmson, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, computer age, creative destruction, data science, debt deflation, deep learning, deskilling, digital divide, disruptive innovation, diversified portfolio, driverless car, Erik Brynjolfsson, factory automation, financial innovation, Flash crash, Ford Model T, Fractional reserve banking, Freestyle chess, full employment, general purpose technology, Geoffrey Hinton, Goldman Sachs: Vampire Squid, Gunnar Myrdal, High speed trading, income inequality, indoor plumbing, industrial robot, informal economy, iterative process, Jaron Lanier, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kenneth Arrow, Khan Academy, Kiva Systems, knowledge worker, labor-force participation, large language model, liquidity trap, low interest rates, low skilled workers, low-wage service sector, Lyft, machine readable, machine translation, manufacturing employment, Marc Andreessen, McJob, moral hazard, Narrative Science, Network effects, new economy, Nicholas Carr, Norbert Wiener, obamacare, optical character recognition, passive income, Paul Samuelson, performance metric, Peter Thiel, plutocrats, post scarcity, precision agriculture, price mechanism, public intellectual, Ray Kurzweil, rent control, rent-seeking, reshoring, RFID, Richard Feynman, Robert Solow, Rodney Brooks, Salesforce, Sam Peltzman, secular stagnation, self-driving car, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, single-payer health, software is eating the world, sovereign wealth fund, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Steven Pinker, strong AI, Stuxnet, technological singularity, telepresence, telepresence robot, The Bell Curve by Richard Herrnstein and Charles Murray, The Coming Technological Singularity, The Future of Employment, the long tail, Thomas L Friedman, too big to fail, Tragedy of the Commons, Tyler Cowen, Tyler Cowen: Great Stagnation, uber lyft, union organizing, Vernor Vinge, very high income, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, women in the workforce

As the competing search algorithms reel in hundreds of possible answers, Watson begins to rank and compare them. One technique used by the machine is to plug the potential answer into the original clue so that it forms a statement, and then go back out to the reference material and look for corroborating text. So if one of the search algorithms manages to come up with the correct response “resignation,” Watson might then search its dataset for a statement something like “Secretary Chase just submitted resignation to Lincoln for the third time.” It would find plenty of close matches, and the computer’s confidence in that particular answer would rise.

Programming Collective Intelligence by Toby Segaran

algorithmic management, always be closing, backpropagation, correlation coefficient, Debian, en.wikipedia.org, Firefox, full text search, functional programming, information retrieval, PageRank, prediction markets, recommendation engine, slashdot, social bookmarking, sparse data, Thomas Bayes, web application

You can try running this on the matrix m1*m2 from your session to see if the algorithm finds a solution similar to the original matrix: >>>import nmf >>> w,h= nmf.factorize(m1*m2,pc=3,iter=100) 7632.94395925 0.0364091326734 ... 1.12810164789e-017 6.8747907867e-020 >>> w*h matrix([[ 22., 28.], [ 49., 64.]]) >>> m1*m2 matrix([[22, 28], [49, 64]]) The algorithm manages to find a weights and features matrix that multiplies together perfectly to match the original. You can also try this on the articles matrix to see how well it can extract the important features (this may take some time): >>>v=matrix(wordmatrix) >>> weights,feat=nmf.factorize(v,pc=20,iter=50) 1712024.47944 2478.13274637 2265.75996871 2229.07352131 2211.42204622 The variable feat now holds the features of the news articles, and weights holds the values that indicate how much each feature applies to each article.

Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All by Robert Elliott Smith

"World Economic Forum" Davos, Ada Lovelace, adjacent possible, affirmative action, AI winter, Alfred Russel Wallace, algorithmic bias, algorithmic management, AlphaGo, Amazon Mechanical Turk, animal electricity, autonomous vehicles, behavioural economics, Black Swan, Brexit referendum, British Empire, Cambridge Analytica, cellular automata, Charles Babbage, citizen journalism, Claude Shannon: information theory, combinatorial explosion, Computing Machinery and Intelligence, corporate personhood, correlation coefficient, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, desegregation, discovery of DNA, disinformation, Douglas Hofstadter, Elon Musk, fake news, Fellow of the Royal Society, feminist movement, Filter Bubble, Flash crash, Geoffrey Hinton, Gerolamo Cardano, gig economy, Gödel, Escher, Bach, invention of the wheel, invisible hand, Jacquard loom, Jacques de Vaucanson, John Harrison: Longitude, John von Neumann, Kenneth Arrow, Linda problem, low skilled workers, Mark Zuckerberg, mass immigration, meta-analysis, mutually assured destruction, natural language processing, new economy, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, On the Economy of Machinery and Manufactures, p-value, pattern recognition, Paul Samuelson, performance metric, Pierre-Simon Laplace, post-truth, precariat, profit maximization, profit motive, Silicon Valley, social intelligence, statistical model, Stephen Hawking, stochastic process, Stuart Kauffman, telemarketer, The Bell Curve by Richard Herrnstein and Charles Murray, The Future of Employment, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Thomas Malthus, traveling salesman, Turing machine, Turing test, twin studies, Vilfredo Pareto, Von Neumann architecture, warehouse robotics, women in the workforce, Yochai Benkler

By 2011 there were a reported 500,000 Turkers located in 190 different countries and as The Atlantic reported in 2018 most of these workers are isolated and living in areas where traditional employment has dried up.6 Employed as contractors, Turkers have no labour protections and aren’t subject to minimum wage laws. Turkers are the poorly paid backstop for algorithmic imperfections, in much the same way workers in the early industrial era were paid poorly to service machines. Throughout the new ‘gig’ economy, there are people working with limited protections, responding to algorithmic management. The drivers of services like Deliveroo and Uber have their work assigned by algorithms and do the part of their task that the algorithms simply cannot do: speedy delivery to the door, in navigation situations (on bike and on foot) that are unlikely to be effectively addressed by autonomous vehicles anytime soon (although that, too, is wildly promised).

Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media by Tarleton Gillespie

4chan, A Declaration of the Independence of Cyberspace, affirmative action, Airbnb, algorithmic bias, algorithmic management, AltaVista, Amazon Mechanical Turk, borderless world, Burning Man, complexity theory, conceptual framework, crowdsourcing, deep learning, do what you love, Donald Trump, drone strike, easy for humans, difficult for computers, Edward Snowden, eternal september, fake news, Filter Bubble, Gabriella Coleman, game design, gig economy, Google Glasses, Google Hangouts, hiring and firing, Ian Bogost, independent contractor, Internet Archive, Jean Tirole, John Gruber, Kickstarter, Mark Zuckerberg, mass immigration, Menlo Park, Minecraft, moral panic, multi-sided market, Netflix Prize, Network effects, pattern recognition, peer-to-peer, power law, real-name policy, recommendation engine, Rubik’s Cube, Salesforce, sharing economy, Silicon Valley, Skype, slashdot, Snapchat, social graph, social web, Steve Jobs, Stewart Brand, TED Talk, Telecommunications Act of 1996, two-sided market, WikiLeaks, Yochai Benkler

Others worried that filtering software was being installed by school administrators and office managers, allowing them to filter the web traffic of an entire school body or all their employees, often without the users’ consent. Social media platforms can incorporate the logic of filtering to a much more sophisticated degree. Platforms are intricate, algorithmically managed visibility machines.20 They grant and organize visibility, not just by policy but by design: sorting and delivering information in the form of profiles, news feeds, threads, channels, categories, updates, notifications. Some content stays where you first posted it, some content is vaulted to the front page, some is delivered to your followers or friends—as you direct it, or as algorithms determine.

Don't Be Evil: How Big Tech Betrayed Its Founding Principles--And All of US by Rana Foroohar

"Susan Fowler" uber, "World Economic Forum" Davos, accounting loophole / creative accounting, Airbnb, Alan Greenspan, algorithmic bias, algorithmic management, AltaVista, Andy Rubin, autonomous vehicles, banking crisis, barriers to entry, behavioural economics, Bernie Madoff, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, book scanning, Brewster Kahle, Burning Man, call centre, Cambridge Analytica, cashless society, clean tech, cloud computing, cognitive dissonance, Colonization of Mars, computer age, corporate governance, creative destruction, Credit Default Swap, cryptocurrency, data is the new oil, data science, deal flow, death of newspapers, decentralized internet, Deng Xiaoping, digital divide, digital rights, disinformation, disintermediation, don't be evil, Donald Trump, drone strike, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Etonian, Evgeny Morozov, fake news, Filter Bubble, financial engineering, future of work, Future Shock, game design, gig economy, global supply chain, Gordon Gekko, Great Leap Forward, greed is good, income inequality, independent contractor, informal economy, information asymmetry, intangible asset, Internet Archive, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, junk bonds, Kenneth Rogoff, life extension, light touch regulation, low interest rates, Lyft, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, Menlo Park, military-industrial complex, move fast and break things, Network effects, new economy, offshore financial centre, PageRank, patent troll, Paul Volcker talking about ATMs, paypal mafia, Peter Thiel, pets.com, price discrimination, profit maximization, race to the bottom, recommendation engine, ride hailing / ride sharing, Robert Bork, Sand Hill Road, search engine result page, self-driving car, shareholder value, sharing economy, Sheryl Sandberg, Shoshana Zuboff, side hustle, Sidewalk Labs, Silicon Valley, Silicon Valley startup, smart cities, Snapchat, SoftBank, South China Sea, sovereign wealth fund, Steve Bannon, Steve Jobs, Steven Levy, stock buybacks, subscription business, supply-chain management, surveillance capitalism, TaskRabbit, tech billionaire, tech worker, TED Talk, Telecommunications Act of 1996, The Chicago School, the long tail, the new new thing, Tim Cook: Apple, too big to fail, Travis Kalanick, trickle-down economics, Uber and Lyft, Uber for X, uber lyft, Upton Sinclair, warehouse robotics, WeWork, WikiLeaks, zero-sum game

That varies based on the algorithm; according to my own anecdotal interviews with drivers in NYC, it has been decreasing as Uber has built its market share, and is around 20 percent now, as opposed to roughly 30 percent for the local independent cab services that some people in the neighborhood still use. Uber touts its drivers as “free and independent” contractors, yet thanks to its automated algorithmic management system, the company is able to control how they work and penalize them when their behaviors deviate from what might be most profitable—for Uber.10 Using artificial intelligence, Uber is able to identify a class of consumers that might be willing to pay more than others for rides, depending on their zip codes.

Complexity: A Guided Tour by Melanie Mitchell

Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Albert Michelson, Alfred Russel Wallace, algorithmic management, anti-communist, Arthur Eddington, Benoit Mandelbrot, bioinformatics, cellular automata, Claude Shannon: information theory, clockwork universe, complexity theory, computer age, conceptual framework, Conway's Game of Life, dark matter, discrete time, double helix, Douglas Hofstadter, Eddington experiment, en.wikipedia.org, epigenetics, From Mathematics to the Technologies of Life and Death, Garrett Hardin, Geoffrey West, Santa Fe Institute, Gregor Mendel, Gödel, Escher, Bach, Hacker News, Hans Moravec, Henri Poincaré, invisible hand, Isaac Newton, John Conway, John von Neumann, Long Term Capital Management, mandelbrot fractal, market bubble, Menlo Park, Murray Gell-Mann, Network effects, Norbert Wiener, Norman Macrae, Paul Erdős, peer-to-peer, phenotype, Pierre-Simon Laplace, power law, Ray Kurzweil, reversible computing, scientific worldview, stem cell, Stuart Kauffman, synthetic biology, The Wealth of Nations by Adam Smith, Thomas Malthus, Tragedy of the Commons, Turing machine

Particles give us something we could not get by looking at the cellular automaton rule or the cellular automaton’s space-time behavior alone: they allow us to explain, in information-processing terms, how a cellular automaton performs a computation. Note that particles are a description imposed by us (the scientists) rather than anything explicit taking place in a cellular automaton or used by the genetic algorithm to evolve cellular automata. But somehow the genetic algorithm managed to evolve a rule whose behavior can be explained in terms of information-processing particles. Indeed, the language of particles and their interactions form an explanatory vocabulary for decentralized computation in the context of one-dimensional cellular automata. Something like this language may be what Stephen Wolfram was looking for when he posed the last of his “Twenty Problems in the Theory of Cellular Automata”: “What higher-level descriptions of information processing in cellular automata can be given?”

Dangerous Ideas: A Brief History of Censorship in the West, From the Ancients to Fake News by Eric Berkowitz

Albert Einstein, algorithmic management, anti-communist, Ayatollah Khomeini, Big Tech, Black Lives Matter, Bonfire of the Vanities, borderless world, Brexit referendum, British Empire, Charlie Hebdo massacre, Chelsea Manning, colonial rule, coronavirus, COVID-19, deplatforming, disinformation, Donald Trump, Edward Snowden, Evgeny Morozov, fake news, Filter Bubble, high-speed rail, Index librorum prohibitorum, Jeff Bezos, Julian Assange, lockdown, Mark Zuckerberg, microaggression, Mikhail Gorbachev, Minecraft, New Urbanism, post-truth, pre–internet, QAnon, Ralph Nader, Saturday Night Live, Silicon Valley, source of truth, Steve Bannon, surveillance capitalism, undersea cable, W. E. B. Du Bois, WikiLeaks

The highest function of the news, as the saying goes, is to speak truth to power, and time and again that goal has been met. However, Internet platforms have become virtual states unto themselves, but without the duties of states. No one voted for them, nor do we know exactly how they and their algorithms manage our speech. Yet they decide which of their billions of users will be heard and by whom, and which will not. That abuses occur countless times daily can be no surprise. They are built into the system. It comes down to money—what the technology writer Charlie Warzel calls the “original sin” of Big Tech’s prioritization of growth over the interests of users.58 When an aging senator asked Facebook’s Mark Zuckerberg in 2018 how the platform can be free to users, Zuckerberg replied (to snickers worldwide), “Senator, we run ads.”

The Wires of War: Technology and the Global Struggle for Power by Jacob Helberg

"World Economic Forum" Davos, 2021 United States Capitol attack, A Declaration of the Independence of Cyberspace, active measures, Affordable Care Act / Obamacare, air gap, Airbnb, algorithmic management, augmented reality, autonomous vehicles, Berlin Wall, Bernie Sanders, Big Tech, bike sharing, Black Lives Matter, blockchain, Boris Johnson, Brexit referendum, cable laying ship, call centre, Cambridge Analytica, Cass Sunstein, cloud computing, coronavirus, COVID-19, creative destruction, crisis actor, data is the new oil, data science, decentralized internet, deep learning, deepfake, deglobalization, deindustrialization, Deng Xiaoping, deplatforming, digital nomad, disinformation, don't be evil, Donald Trump, dual-use technology, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, fail fast, fake news, Filter Bubble, Francis Fukuyama: the end of history, geopolitical risk, glass ceiling, global pandemic, global supply chain, Google bus, Google Chrome, GPT-3, green new deal, information security, Internet of things, Jeff Bezos, Jeffrey Epstein, John Markoff, John Perry Barlow, knowledge economy, Larry Ellison, lockdown, Loma Prieta earthquake, low earth orbit, low skilled workers, Lyft, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Mary Meeker, Mikhail Gorbachev, military-industrial complex, Mohammed Bouazizi, move fast and break things, Nate Silver, natural language processing, Network effects, new economy, one-China policy, open economy, OpenAI, Parler "social media", Peter Thiel, QAnon, QR code, race to the bottom, Ralph Nader, RAND corporation, reshoring, ride hailing / ride sharing, Ronald Reagan, Russian election interference, Salesforce, Sam Altman, satellite internet, self-driving car, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, smart grid, SoftBank, Solyndra, South China Sea, SpaceX Starlink, Steve Jobs, Steven Levy, Stuxnet, supply-chain attack, Susan Wojcicki, tech worker, techlash, technoutopianism, TikTok, Tim Cook: Apple, trade route, TSMC, Twitter Arab Spring, uber lyft, undersea cable, Unsafe at Any Speed, Valery Gerasimov, vertical integration, Wargames Reagan, Westphalian system, white picket fence, WikiLeaks, Y Combinator, zero-sum game

But the truth is that our data—whether you’re buying a pair of shoes online or looking up a medical diagnosis on WebMD—does pass through tangible telecommunications lines and data storage centers. We speak of “the cloud,” but the reality is much more concrete. You might even call the Internet a series of tubes with algorithms managing the flow of data. The tubes analogy is worth keeping in mind because—even while scoffing at the notion that the Internet is anything like an ordinary utility—we frequently imbue our telecom system with the same faith. We don’t think twice about how the information that flows into and out of our various devices travels from Point A to Point B.

Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity by Daron Acemoglu, Simon Johnson

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 4chan, agricultural Revolution, AI winter, Airbnb, airline deregulation, algorithmic bias, algorithmic management, Alignment Problem, AlphaGo, An Inconvenient Truth, artificial general intelligence, augmented reality, basic income, Bellingcat, Bernie Sanders, Big Tech, Bletchley Park, blue-collar work, British Empire, carbon footprint, carbon tax, carried interest, centre right, Charles Babbage, ChatGPT, Clayton Christensen, clean water, cloud computing, collapse of Lehman Brothers, collective bargaining, computer age, Computer Lib, Computing Machinery and Intelligence, conceptual framework, contact tracing, Corn Laws, Cornelius Vanderbilt, coronavirus, corporate social responsibility, correlation does not imply causation, cotton gin, COVID-19, creative destruction, declining real wages, deep learning, DeepMind, deindustrialization, Demis Hassabis, Deng Xiaoping, deskilling, discovery of the americas, disinformation, Donald Trump, Douglas Engelbart, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, energy transition, Erik Brynjolfsson, European colonialism, everywhere but in the productivity statistics, factory automation, facts on the ground, fake news, Filter Bubble, financial innovation, Ford Model T, Ford paid five dollars a day, fulfillment center, full employment, future of work, gender pay gap, general purpose technology, Geoffrey Hinton, global supply chain, Gordon Gekko, GPT-3, Grace Hopper, Hacker Ethic, Ida Tarbell, illegal immigration, income inequality, indoor plumbing, industrial robot, interchangeable parts, invisible hand, Isaac Newton, Jacques de Vaucanson, James Watt: steam engine, Jaron Lanier, Jeff Bezos, job automation, Johannes Kepler, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph-Marie Jacquard, Kenneth Arrow, Kevin Roose, Kickstarter, knowledge economy, labor-force participation, land reform, land tenure, Les Trente Glorieuses, low skilled workers, low-wage service sector, M-Pesa, manufacturing employment, Marc Andreessen, Mark Zuckerberg, megacity, mobile money, Mother of all demos, move fast and break things, natural language processing, Neolithic agricultural revolution, Norbert Wiener, NSO Group, offshore financial centre, OpenAI, PageRank, Panopticon Jeremy Bentham, paperclip maximiser, pattern recognition, Paul Graham, Peter Thiel, Productivity paradox, profit maximization, profit motive, QAnon, Ralph Nader, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Robert Bork, Robert Gordon, Robert Solow, robotic process automation, Ronald Reagan, scientific management, Second Machine Age, self-driving car, seminal paper, shareholder value, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, social intelligence, Social Responsibility of Business Is to Increase Its Profits, social web, South Sea Bubble, speech recognition, spice trade, statistical model, stem cell, Steve Jobs, Steve Wozniak, strikebreaker, subscription business, Suez canal 1869, Suez crisis 1956, supply-chain management, surveillance capitalism, tacit knowledge, tech billionaire, technoutopianism, Ted Nelson, TED Talk, The Future of Employment, The Rise and Fall of American Growth, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, theory of mind, Thomas Malthus, too big to fail, total factor productivity, trade route, transatlantic slave trade, trickle-down economics, Turing machine, Turing test, Twitter Arab Spring, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, union organizing, universal basic income, Unsafe at Any Speed, Upton Sinclair, upwardly mobile, W. E. B. Du Bois, War on Poverty, WikiLeaks, wikimedia commons, working poor, working-age population

To explain this problem, it is useful to have a broader understanding of the overfitting problem, based on using irrelevant or nonpermanent features of an application. Consider the task of distinguishing wolves from huskies. Although humans are excellent at this task, it turns out to be a difficult one for AI. When some algorithms managed to achieve good performance, it was later understood that this was thanks to overfitting: huskies were recognized from urban backgrounds, such as nice lawns and fire hydrants, and wolves from natural backgrounds, such as snowy mountains. These are irrelevant characteristics in two fundamental senses.