Law Commons^™

Between Risk Mitigation And Labour Rights Enforcement: Assessing The Transatlantic Race To Govern Ai-Driven Decision-Making Through A Comparative Lens, Valerio De Stefano, Antonio Aloisi

Articles & Book Chapters

In this article, we provide an overview of efforts to regulate the various phases of the artificial intelligence (AI) life cycle. In doing so, we examine whether—and, if so, to what extent—highly fragmented legal frameworks are able to provide safeguards capable of preventing the dangers that stem from AI- and algorithm-driven organisational practices. We critically analyse related developments at the European Union (EU) level, namely the General Data Protection Regulation, the draft AI Regulation, and the proposal for a Directive on improving working conditions in platform work. We also consider bills and regulations proposed or adopted in the United States …

Regulating Ai At Work: Labour Relations, Automation, And Algorithmic Management, Valerio De Stefano, Virginia Doellgast

Articles & Book Chapters

Recent innovations in artificial intelligence (AI) have been at the core of massive technological changes that are transforming work. AI is now widely used to automate business processes and replace labour-intensive tasks while changing the skill demands for those that remain. AI-based tools are also deployed to invasively monitor worker conduct and to automate HR management processes.

Through the dual lens of comparative labour law and employment relations research, the articles in this special issue of Transfer investigate the role of collective bargaining and government policy in shaping strategies to deploy new digital and AI-based technologies at work. Together, they …

Algorithmic Management And Collective Bargaining, Valerio De Stefano, Simon Taes

Articles & Book Chapters

This article addresses the challenges raised by the introduction of algorithmic management and artificial intelligence in the world of work, focusing on the risks that new managerial technologies present for fundamental rights and principles, such as non-discrimination, freedom of association and the right to privacy. The article argues that collective bargaining is the most suitable regulatory instrument for responding to these challenges, and that current EU legislative initiatives do not adequately recognise the role of collective bargaining in this area. It also maps current initiatives undertaken by national trade union movements in Europe to govern algorithmic management.

Machine Monitoring Of Workers: A Brave New Workplace, Anne M. Lofaso

Law Faculty Scholarship

No abstract provided.

Artificial Intelligence And The Challenges Of Workplace Discrimination And Privacy, Pauline Kim, Matthew T. Bodie

Scholarship@WashULaw

Employers are increasingly relying on artificially intelligent (AI) systems to recruit, select, and manage their workforces, raising fears that these systems may subject workers to discriminatory, invasive, or otherwise unfair treatment. This article reviews those concerns and provides an overview of how current laws may apply, focusing on two particular problems: discrimination on the basis of protected characteristics like race, sex, or disability, and the invasion of workers’ privacy engendered by workplace AI systems. It discusses the ways in which relying on AI to make personnel decisions can produce discriminatory outcomes and how current law might apply. It then explores …

Ai And Inequality, Pauline Kim

Scholarship@WashULaw

This Chapter examines the social consequences of artificial intelligence (AI) when it is used to make predictions about people in contexts like employment, housing and criminal law enforcement. Observers have noted the potential for erroneous or arbitrary decisions about individuals; however, the growing use of predictive AI also threatens broader social harms. In particular, these technologies risk increasing inequality by reproducing or exacerbating the marginalization of historically disadvantaged groups, and by reinforcing power hierarchies that contribute to economic inequality. Using the employment context as the primary example, this Chapter explains how AI-powered tools that are used to recruit, hire and …

Future Work, Jeffrey M. Hirsch

AI-DR Collection

The Industrial Revolution. The Digital Age. These revolutions radically altered the workplace and society. We may be on the cusp of a new era—one that will rival or even surpass these historic disruptions. Technology such as artificial intelligence, robotics, virtual reality, and cutting-edge monitoring devices are developing at a rapid pace. These technologies have already begun to infiltrate the workplace and will continue to do so at ever increasing speed and breadth.

This Article addresses the impact of these emerging technologies on the workplace of the present and the future. Drawing upon interviews with leading technologists, the Article explains the …

Big Data And Artificial Intelligence: New Challenges For Workplace Equality, Pauline Kim

Scholarship@WashULaw

This essay contains remarks delivered in a keynote speech at the University of Louisville Brandeis School of Law’s 35th Annual Carl A. Warns and Edwin R. Render Labor and Employment Law Institute. Big data and artificial intelligence are increasingly being used by employers in their human resources processes in ways that control access to employment opportunities. This essay describes some of those developments and explains how practices like targeted online recruitment strategies and the use of hiring algorithms to screen applicants raise a significant risk of discriminating against protected groups such as women and racial minorities. It then considers some …

Taxation Of Automation And Artificial Intelligence As A Tool Of Labour Policy, Vincent Ooi, Glendon Goh

Research Collection Yong Pung How School Of Law

Rapid developments in automation technology pose a risk of mass displacement of human labour, resulting in the need to support and retrain displaced workers (a negative externality). We propose an “automation tax” that would slow the adoption of automation technology in appropriate circumstances, giving workers and social support systems time to adapt. This could be easily implemented through changes to the existing schedular system of depreciation/ capital allowances, reducing the uncertainty of its application and implementation costs. Such a system would be flexible enough to keep up with rapid technological developments. Two main dimensions may be adjusted to produce intended …