algorithmic management Concepts

The Means of Prediction: How AI Really Works (And Who Benefits)

by Maximilian Kasy · 15 Jan 2025 · 209pp · 63,332 words

of autocratic surveillance states. One domain in which AI is deployed in society is the workplace. AI is used in robotized Amazon warehouses, in the algorithmic management of Uber drivers, and in the screening of job candidates by large companies. AI is also used in consequential domains outside the workplace, including the

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important for discussions about algorithmic bias and fairness. Another important domain is labor law, which can impose restrictions on surveillance in the workplace, and on algorithmic management by AI, for instance for gig workers such as those driving for Uber. These are some of the existing levers that courts and regulators can

Hello World: Being Human in the Age of Algorithms

by Hannah Fry · 17 Sep 2018 · 296pp · 78,631 words

Uberland: How Algorithms Are Rewriting the Rules of Work

by Alex Rosenblat · 22 Oct 2018 · 343pp · 91,080 words

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

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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

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-based piecework, and some workers value them.31 Uber’s platform manifests a profound tension: the company seeks to standardize work for the masses through algorithmic management while, at the same time, distancing itself from responsibility for workers. The popularity of Uber among passengers has been central to public support for the

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new era of work through its practice of managing its drivers with algorithms, but the factors that influence how drivers experience their work extend beyond algorithmic management. Regional contexts, drivers’ own motivations and experiences of work, and their level of investment in the job all affect the ways they perceive its benefits

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written by an algorithm. By distancing its employment relationship to drivers through the framework of entrepreneurship, Uber masks its own methods and the power of algorithmic management to shape the nature of their work. Drivers’ experiences demonstrate the gap between rhetoric and reality when Uber talks about being a beacon of entrepreneurial

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poetic myth of full independence. In addition, they don’t necessarily feel exploited if their entrepreneurial decision-making is limited by Uber’s system of algorithmic management. Nonetheless, for a subset of people who are driving for a living, the tensions between the promise of entrepreneurship and the realities of how they

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of blind passenger acceptance ensures that passengers receive reliable service. The company seems unconcerned that its practices severely limit drivers’ ability to optimize their earnings. Algorithmic management is a system that works for the company—simple, efficient, and bureaucratic. But its drivers suffer as they are forced to accept the odds that

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reflect a host of unexpected biases. 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

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, 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. Despite the fact that its platform both faces consumers and organizes labor, Uber simply takes many of the same consumer

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-facing algorithmic management practices from Silicon Valley and applies them to an employment context. To understand how extractive practices have been woven into Uber’s system requires leaving

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society about the role of technology tools in our daily lives.22 Nonetheless, the consumer-facing platforms of Silicon Valley downplay the role of opaque algorithmic management when it applies to their “end users.” Similarly, although Uber acts as a supposedly neutral middleman, it violates the spirit of neutrality when it adjusts

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they are used to manage labor in an employment context. WHEN THE ALGORITHMIC BOSS DECEIVES: WAGE THEFT, PRICE GOUGING, AND UNPAID LABOR Silicon Valley spins algorithmic management as being neutral, yet we’ve now seen why this claim is not true. But there is a range of deceptive algorithmic practices in the

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algorithmic neutrality has done nothing to change older practices of price discrimination. The “neutrality” of algorithms has different implications in the context of employment and algorithmic management. Surge pricing is used as a tactic to provide drivers with the hope of extra wages, which they may never receive (see figure 18). Drivers

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blends a high-confidence assessment of real-time demand with a lower-confidence recommendation of predictive high demand.45 The friction that drivers experience under algorithmic management reveals the changing dynamics experienced by users of technology services more generally.46 In particular, these experiences illustrate that while

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algorithmic management may produce a broad social benefit for the majority of platform users, it can deceive individual users along the way.47 The promise of a

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services. What they can do with that data is constrained by the rules set by their manager, which directly affects their livelihoods. This type of algorithmic management highlights the fact that “neutral” algorithms that appear to provide an objective data analysis of supply and demand can manipulate drivers. Although

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algorithmic management of Uber’s drivers shows us how unneutral platforms can take advantage of workers, Uber’s practices shed light on how the datacentric algorithms that

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of nudges urging them to behave in particular ways, as discussed in chapter 4.7 HOW UBER TREATS DRIVERS LIKE CONSUMERS We’ve seen how algorithmic management can produce biases and manipulate consumers of Facebook, Google, and the products of other Silicon Valley companies. Similarly, inequities among drivers can emerge when algorithmic

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provides us with a lens for examining inequities that emerge and multiply in a tech-driven world. The question for us to consider is whether algorithmic management creates a qualitative distinction between work and consumption. Uber’s arguments actually articulate dynamic changes in how employment and consumption are negotiated in digital spaces

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benefits or security. The affective benefits, like the community you build with other drivers during down periods, continue.”2 Nevertheless, some of the drawbacks of algorithmic management center on information scarcity, rather than on discussions of employment benefits. Drivers don’t have an employee handbook when they start out: instead, they learn

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a test feature—can spread quickly. If misinformation is spread by the same channels, the credibility of driver discourse might be jeopardized. These dynamics, from algorithmic management to networked resistance, illustrate that Uber makes more than a splash in society. The company creates an infinite series of ripple effects in every place

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conflicted relationship between Uber and its drivers is an example of how labor relations are being shaped in our new, digital age. The rise of algorithmic management of consumers is prominent across Silicon Valley’s data-driven technologies. You can’t go far in daily life without encountering these systems: GPS navigation

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

by David de Cremer · 25 May 2020 · 241pp · 70,307 words

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

Nexus: A Brief History of Information Networks From the Stone Age to AI

by Yuval Noah Harari · 9 Sep 2024 · 566pp · 169,013 words

Virtual Competition

by Ariel Ezrachi and Maurice E. Stucke · 30 Nov 2016

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

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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

Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity

by Daron Acemoglu and Simon Johnson · 15 May 2023 · 619pp · 177,548 words

What Algorithms Want: Imagination in the Age of Computing

by Ed Finn · 10 Mar 2017 · 285pp · 86,853 words

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

Human + Machine: Reimagining Work in the Age of AI

by Paul R. Daugherty and H. James Wilson · 15 Jan 2018 · 523pp · 61,179 words

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

Radical Technologies: The Design of Everyday Life

by Adam Greenfield · 29 May 2017 · 410pp · 119,823 words

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

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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

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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