A received wisdom is that automated decision-making serves as an anti-bias intervention. The conceit is that removing humans from the decision-making process will also eliminate human bias. The paradox, however, is that in some instances, automated decision-making has served to replicate and amplify bias. With a case study of the algorithmic capture of hiring as heuristic device, this Article provides a taxonomy of problematic features associated with algorithmic decision-making as anti-bias intervention and argues that those features are at odds with the fundamental principle of equal opportunity in employment. To examine these problematic features within the context of algorithmic hiring and to explore potential legal approaches to rectifying them, the Article brings together two streams of legal scholarship: law and technology studies and employment & labor law.Counterintuitively, the Article contends that the framing of algorithmic bias as a technical problem is misguided. Rather, the Article's central claim is that bias is introduced in the hiring process, in large part, due to an American legal tradition of deference to employers, especially allowing for such nebulous hiring criterion as “cultural fit.” The Article observes the lack of legal frameworks that take into account the emerging technological capabilities of hiring tools which make it difficult to detect disparate impact. The Article thus argues for a re-thinking of legal frameworks that take into account both the liability of employers and those of the makers of algorithmic hiring systems who, as brokers, owe a fiduciary duty of care. Particularly related to Title VII, the Article proposes that in legal reasoning corollary to extant tort doctrines, an employer's failure to audit and correct its automated hiring platforms for disparate impact could serve as prima facie evidence of discriminatory intent, leading to the development of the doctrine of discrimination per se. The article also considers other approaches separate from employment law such as establishing consumer legal protections for job applicants that would mandate their access to the dossier of information consulted by automated hiring systems in making the employment decision.

This book argues that technological developments in the workplace have 'quantified' the modern worker to the detriment of social equality.

Should we regulate artificial intelligence? Can we? From self-driving cars and high-speed trading to algorithmic decision-making, the way we live, work, and play is increasingly dependent on AI systems that operate with diminishing human intervention. These fast, autonomous, and opaque machines offer great benefits – and pose significant risks. This book examines how our laws are dealing with AI, as well as what additional rules and institutions are needed – including the role that AI might play in regulating itself. Drawing on diverse technologies and examples from around the world, the book offers lessons on how to manage risk, draw red lines, and preserve the legitimacy of public authority. Though the prospect of AI pushing beyond the limits of the law may seem remote, these measures are useful now – and will be essential if it ever does.

This volume tackles a quickly-evolving field of inquiry, mapping the existing discourse as part of a general attempt to place current developments in historical context; at the same time, breaking new ground in taking on novel subjects and pursuing fresh approaches. The term "A.I." is used to refer to a broad range of phenomena, from machine learning and data mining to artificial general intelligence. The recent advent of more sophisticated AI systems, which function with partial or full autonomy and are capable of tasks which require learning and 'intelligence', presents difficult ethical questions, and has drawn concerns from many quarters about individual and societal welfare, democratic decision-making, moral agency, and the prevention of harm. This work ranges from explorations of normative constraints on specific applications of machine learning algorithms today-in everyday medical practice, for instance-to reflections on the (potential) status of AI as a form of consciousness with attendant rights and duties and, more generally still, on the conceptual terms and frameworks necessarily to understand tasks requiring intelligence, whether "human" or "A.I."

This book explores the opportunities, challenges, and effective approaches to organizational change regarding diversity, equity, inclusion, and belonging. Featuring application-based case studies and practical guidelines for meaningful organizational change, this book problematizes some of the current DEI initiatives in today’s organizations. It examines multiple forms of diversity (e.g., race, age, and mental health) from a variety of perspectives (e.g., leadership and employee), with case studies that demonstrate how changemaking efforts can be reimagined and implemented in better, more nuanced, and more sustainable ways to produce meaningful organizational change. Through these case studies, readers learn from organizations’ successes and failures in their attempts to implement DEI practices. Each chapter concludes with explicit practical implications and/or actionable recommendations for organizational changemaking. This text will make an impactful addition to courses in communication and diversity or organizational communication/change at the advanced undergraduate or graduate level, and will be an essential guide for professionals wishing to lead change in their organizations.

The current sharing economy suffers from system-wide deficiencies even as it produces distinctive benefits and advantages for some participants. The first generation of sharing markets has left us to question: Will there be any workers in the sharing economy? Can we know enough about these technologies to regulate them? Is there any way to avoid the monopolization of assets, information, and wealth? Using convergent, transdisciplinary perspectives, this volume examines the challenge of reengineering a sharing economy that is more equitable, democratic, sustainable, and just. The volume enhances the reader's capacity for integrating applicable findings and theories in business, law and social science into ethical engineering design and practice. At the same time, the book helps explain how technological innovations in the sharing economy create value for different stakeholders and how they impact society at large. Reengineering the Sharing Economy is also available as Open Access on Cambridge Core.

Digital hyperconnectivity is a defining fact of our time. The Silicon Valley dream of universal connection – the dream of connecting everyone and everything to everyone and everything else, everywhere and all the time – is rapidly becoming a reality. In this wide-ranging and sharply argued book, Rogers Brubaker develops an original interpretive account of the pervasive and unsettling changes brought about by hyperconnectivity. He traces transformations of the self, social relations, culture, economics, and politics, giving special attention to underexplored themes of abundance, miniaturization, convenience, quantification, and discipline. He shows how hyperconnectivity prepared us for the pandemic and how the pandemic, in turn, has prepared us for an even more fully digitally mediated future. Throughout, Brubaker underscores the ambivalence of digital hyperconnectivity, which opens up many new and exciting possibilities, yet at the same time threatens human freedom and flourishing. Hyperconnectivity and Its Discontents will be essential reading for everyone interested in the constellation of socio-technical forces that are profoundly remaking our world.