Algorithmic discrimination: examining its types and regulatory measures with emphasis on US legal practices - Wang, Xukang; Wu, Ying Cheng; Ji, Xueliang; Fu, Hongpeng (Frontiers in Artificial Intelligence, 2024)
Algorithmic decision-making systems are increasingly being used across various domains, from criminal justice to employment to education. While these systems offer the potential for increased efficiency and objectivity, they also risk perpetuating and amplifying societal biases and discrimination. This paper provides a comprehensive examination of the types of algorithmic discrimination, the legal and regulatory approaches to addressing them, and the challenges involved in ensuring fairness and accountability in automated decisions. Drawing on a systematic literature review, legal document analysis, and comparative case studies, the authors identify five main types of algorithmic bias: bias by algorithmic agents, discrimination based on feature selection, proxy discrimination, disparate impact, and targeted advertising. The paper analyzes the current U.S. legal and regulatory landscape, discussing principled and specific regulations, preventive controls, consequential liability, self-regulation, and heteronomy regulation. It also provides a comparative perspective by examining algorithmic fairness measures in the EU, Canada, Australia, and Asia. Case studies on criminal risk assessment and hiring algorithms illustrate the real-world impacts of algorithmic discrimination. The paper concludes with recommendations for interdisciplinary research, proactive policy development, public awareness, and ongoing monitoring to promote fairness and accountability in algorithmic decision-making. As the use of AI and automated systems expands globally, this work highlights the importance of developing comprehensive, adaptive approaches to combat algorithmic discrimination and ensure the socially responsible deployment of these powerful technologies.