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.

The information revolution has ushered in a data-driven reorganization of the workplace. Big data and AI are used to surveil workers and shift risk. Workplace wellness programs appraise our health. Personality job tests calibrate our mental state. The monitoring of social media and surveillance of the workplace measure our social behavior. With rich historical sources and contemporary examples, The Quantified Worker explores how the workforce science of today goes far beyond increasing efficiency and threatens to erase individual personhood. With exhaustive detail, Ifeoma Ajunwa shows how different forms of worker quantification are enabled, facilitated, and driven by technological advances. Timely and eye-opening, The Quantified Worker advocates for changes in the law that will mitigate the ill effects of the modern workplace.

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.

We already observe the positive effects of AI in almost every field, and foresee its potential to help address our sustainable development goals and the urgent challenges for the preservation of the environment. We also perceive that the risks related to the safety, security, confidentiality, and fairness of AI systems, the threats to free will of possibly manipulative systems, as well as the impact of AI on the economy, employment, human rights, equality, diversity, inclusion, and social cohesion need to be better assessed. The development and use of AI must be guided by principles of social cohesion, environmental sustainability, resource sharing, and inclusion. It has to integrate human rights, and social, cultural, and ethical values of democracy. It requires continued education and training as well as continual assessment of its effects through social deliberation. The “Reflections on AI for Humanity” proposed in this book develop the following issues and sketch approaches for addressing them: How can we ensure the security requirements of critical applications and the safety and confidentiality of data communication and processing? What techniques and regulations for the validation, certification, and audit of AI tools are needed to develop confidence in AI? How can we identify and overcome biases in algorithms? How do we design systems that respect essential human values, ensuring moral equality and inclusion? What kinds of governance mechanisms are needed for personal data, metadata, and aggregated data at various levels? What are the effects of AI and automation on the transformation and social division of labor? What are the impacts on economic structures? What proactive and accommodation measures will be required? How will people benefit from decision support systems and personal digital assistants without the risk of manipulation? How do we design transparent and intelligible procedures and ensure that their functions reflect our values and criteria? How can we anticipate failure and restore human control over an AI system when it operates outside its intended scope? How can we devote a substantial part of our research and development resources to the major challenges of our time such as climate, environment, health, and education?

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.