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Full-Text Articles in Law
Hired By A Machine: Can A New York City Law Enforce Algorithmic Fairness In Hiring Practices?, Lindsey Fuchs
Hired By A Machine: Can A New York City Law Enforce Algorithmic Fairness In Hiring Practices?, Lindsey Fuchs
Fordham Journal of Corporate & Financial Law
Workplace antidiscrimination laws must adapt to address today’s technological realities. If left underregulated, the rapidly expanding role of Artificial Intelligence (“AI”) in hiring practices has the danger of creating new, more obscure modes of discrimination. Companies use these tools to reduce the duration and costs of hiring and potentially attract a larger pool of qualified applicants for their open positions. But how can we guarantee that these hiring tools yield fair outcomes when deployed? These issues are just starting to be addressed at the federal, state, and city levels. This Note tackles whether a new city law can be improved …
Incomprehensible Discrimination, James Grimmelmann, Daniel Westreich
Incomprehensible Discrimination, James Grimmelmann, Daniel Westreich
Cornell Law Faculty Publications
The following (fictional) opinion of the (fictional) Zootopia Supreme Court of the (fictional) State of Zootopia is designed to highlight one particularly interesting issue raised by Solon Barocas and Andrew Selbst in Big Data’s Disparate Impact. Their article discusses many ways in which data-intensive algorithmic methods can go wrong when they are used to make employment and other sensitive decisions. Our vignette deals with one in particular: the use of algorithmically derived models that are both predictive of a legitimate goal and have a disparate impact on some individuals. Like Barocas and Selbst, we think it raises fundamental questions about …