In Chapter 6, we present our reconceptualization of organizational control. We discuss four fundamental shifts in organizations – from face-to-face work to remote work; from stable, full-time work to alternative work arrangements; from human managers to algorithmic control; and from traditional to platform-mediated gig work – and discuss the impact of these shifts on organizational control. Our reconceptualization consists of both a conceptual part, where we advance a configurational approach to model the causal complexity inherent in organizational control, and an empirical part, where we present exemplary archetypes of control configurations across a variety of twenty-first-century organizations, including US trucking companies, GitLab, Amazon warehouses, Uber, and Upwork.

This chapter argues that the classification of workers as independent contractors or employees should be shaped by an overarching question: How much flexibility do individuals have in determining the time, place, price, manner, and frequency of their work? Those who have more control of these variables are more independent than those who must conform to a business owner’s schedule. This approach provides an objective basis for resolving classification disputes, particularly those that arise in the context of the technology-based economy. By minimizing legal uncertainty, a focus on worker flexibility would ensure both that workers receive appropriate protections under existing law and that companies are able to innovate without fear of unknown liabilities.

When agents insert technological systems into their decision-making processes, they can obscure moral responsibility for the results. This can give rise to a distinct moral wrong, which we call “agency laundering.” At root, agency laundering involves obfuscating one’s moral responsibility by enlisting a technology or process to take some action and letting it forestall others from demanding an account for bad outcomes that result. We argue that the concept of agency laundering helps in understanding important moral problems in a number of recent cases involving automated, or algorithmic, decision-systems. We apply our conception of agency laundering to a series of examples, including Facebook’s automated advertising suggestions, Uber’s driver interfaces, algorithmic evaluation of K-12 teachers, and risk assessment in criminal sentencing. We distinguish agency laundering from several other critiques of information technology, including the so-called “responsibility gap,” “bias laundering,” and masking.

This chapter argues that the partial automation of managerial authority should not matter when determining whether platform workers are—or should be treated as—employees or independent contractors under the law. It offers a close reading of an end-user license agreement (EULA) between the on-demand transportation company Uber and its drivers. In particular, the chapter examines the relationship between the EULA and Uber’s use of algorithmic management to design, direct, monitor, evaluate, and compensate drivers’ work. By scrutinizing this relationship through a theory of the “self” implicit in contract law, the chapter shows that the EULA grants Uber rights to direct drivers’ work and determine the principal terms of the agreement as it goes along. This unilateral discretion is difficult to reconcile with what the law expects of an enforceable contract. Rather, it resembles the open-ended authority that the law permits an employer over its employees. Uber exercises its extra-contractual discretion through the App. Algorithmic management enables Uber continually to recalibrate its putative bargain with drivers to the company’s advantage. One lesson of this analysis is that decision makers tasked with evaluating the employment status of platform workers should take more seriously the contractual component of being an “independent contractor.”

This chapter offers an overview of the legal and regulatory landscape impacting gig work in America. It covers recent developments in case law and legislation at city, state, and federal levels, as well as regulatory activity involving administrative actors like the National Labor Relations Board. The legal landscape described here provides the background for the chapters that follow. It argues that there are in fact far fewer laws regulating gig work than one might expect because of the newness of this type of labor exchange, preemption rules that make it difficult for local (and sometimes state) authorities to regulate work, and because non-work issues – consumer protection, taxation, urban infrastructure – have tended to occupy regulators’ attention.

When companies like Uber and TaskRabbit appeared in Silicon Valley, there was a collective media swoon over these new app-based service-delivery corporations and their products. Pundits and journalists made it seem like these companies were ushering in not only an inevitable future, but a desirable one. Their content helped convince the public and regulators that these businesses were different from existing corporations – that they were startups with innovative technology platforms designed to disrupt established firms by efficiently connecting consumers to independent, empowered gig workers. Those in the media normalized and at times generated this rhetoric and framing, which was then taken up by politicians, amplified by academics, and finally enshrined in laws that legalized the business models of these companies. The positive, uncritical coverage prevailed for years and helped pave the way for a handful of companies that represent a tiny fraction of the economy to have an outsized impact on law, mainstream corporate practices, and the way we think about work. The force that powered the swoon was a relatively new and journalistically problematic trend in media: “tech” reporting.

This chapter first provides a framework for understanding recent local government approaches to aligning Uber and Lyft operations with urban transportation policy goals—including improving street safety, improving transportation access, and reducing greenhouse gas emissions. Many of these approaches to setting policy and designing streets are not regulatory per se, though they can and have been used as de facto regulatory strategies. This “implicit” regulatory approach has arisen in part because most local governments in the U.S. lack the formal authority to regulate Uber and Lyft. Furthermore, most local governments also lack the data necessary to develop and/or enforce appropriate regulations of the app-enabled for-hire vehicle industry.

The chapter continues with a case study of how the San Francisco County Transportation Authority, in partnership with researchers at Northeastern University, developed a creative and partnership-driven approach to policy-making in the face of a severe data deficit. Agency staff and University researchers scraped data from the Uber and Lyft application programming interfaces and used those data to better understand how people move in San Francisco County. This work demonstrates the importance of innovative, goal-oriented problem-solving approaches to inform the regulation of increasingly complex city streets.

As the United States tax system continues to grapple with how to tax workers in the gig economy, it confronts a number of questions about the nature and composition of the sector as well as the tax issues confronted by its participants. Many of these questions have proven difficult to answer due to a lack of adequate information. But the answers are important and will shape how tax and other areas of law (such as employment law, labor law, and antitrust) respond to the gig economy. Thus, the question of how to obtain the data and information necessary to formulate sound policies for gig work is vital. This chapter discusses the limitations of quantitative empirical research on the gig economy and argues that incorporating more qualitative approaches will help generate a more comprehensive understanding of the tax policy issues involved. Adoption of a diverse set of research approaches is crucial because the administrative tax return and labor survey data are incomplete and are shaped by prior decisions of gig economy firms and participants. Many questions remain that such quantitative data, by its very nature, cannot answer. This chapter first identifies the key tax issues at stake in the gig economy, including tax administration, worker classification, and tax impacts on workforce decisions. It then discusses the key ways in which quantitative approaches do not fully capture the tax issues at stake. Finally, it details how qualitative research methods such as interviews and case studies can flesh out gaps in the quantitative data, can help interpret quantitative data, and can help answer questions that extend beyond the scope of quantitative data, yielding a richer account of gig economy tax issues than that provided by quantitative tax administrative and labor survey data alone.

This chapter presents the sharing economy phenomenon and studies its main characteristics. It argues that it is a diverse phenomenon with diverse normative implications. There is a tendency in legal academia to focus almost exclusively on Airbnb and Uber, and this tendency flattens the complexity of the phenomenon. The chapter maps the phenomenon based on an institutional analysis, focusing on who owns or controls the property or service. It identifies four main institutional categories: peer-to-peer markets that include both for-profit projects and nonprofit enterprises, commercial companies, communal projects, and governmental programs. The distinction among forms of access is important. Each category raises different legal and policy-oriented challenges. Each category also carries different normative value from the perspective of flexibility.