Learning Algorithms and Discrimination

Book Chapter: Learning Algorithms and Discrimination In RESEARCH HANDBOOK OF ARTIFICIAL INTELLIGENCE AND LAW (Woodrow Barfield & Ugo Pagallo eds.) (2018 Forthcoming)

Baruch College Zicklin School of Business Research Paper No. 2018-04-03

29 Pages Posted: 27 Apr 2018

See all articles by Nizan Geslevich Packin

Nizan Geslevich Packin

City University of NY, Baruch College, Zicklin School of Business; City University of New York - Department of Law

Yafit Lev Aretz

City University of New York - Department of Law

Date Written: April 25, 2018

Abstract

In most accounts of Artificial Intelligence (AI), humans are pitted against the machines, and concerns about relative physical and mental strength populate the discussion. There are, however, numerous processes and shifts that AI stimulates within the human context that are equally essential to uncover as AI technology gradually penetrates various markets. In this chapter, we focus on data-driven discrimination, explaining its origins, scope, and current treatment under the law. We explain how errors, biases, and opaqueness are endemic to AI systems, which rely heavily on data collected and analyzed. Nevertheless, it is unclear whether and to what extent existing anti-discrimination doctrines such as disparate impact apply to biases resulting from algorithmic systems. We also discuss examples of discriminating AI technologies in five dominant fields: finance, employment and labor, health and healthcare, education, and the legal system. These examples demonstrate that while AI enjoys a scientific glory of improving human performance, streamlining operations, and saving costs, AI also suffers from inherent biases and inaccuracies that risk perpetuating social injustices by systemizing discrimination. To capitalize on the innovative benefits that AI has to offer and mitigate discriminatory concerns, proper scholarly awareness, industry cooperation, and regulatory attention must accompany the introduction of AI technologies.

Keywords: Algorithms, Discrimination, Black Box, Choice-making, Machine Learning, Artificial Intelligence, Data Accuracy, Big Data, Disparate Impact, Due Process, Opaqueness, Equal Credit Opportunity Act, General Data Protection Regulation, Social Biases, Financial Service, Employment, Health, Education

Suggested Citation

Packin, Nizan Geslevich and Lev Aretz, Yafit, Learning Algorithms and Discrimination (April 25, 2018). Book Chapter: Learning Algorithms and Discrimination In RESEARCH HANDBOOK OF ARTIFICIAL INTELLIGENCE AND LAW (Woodrow Barfield & Ugo Pagallo eds.) (2018 Forthcoming); Baruch College Zicklin School of Business Research Paper No. 2018-04-03. Available at SSRN: https://ssrn.com/abstract=3168579

Nizan Geslevich Packin (Contact Author)

City University of NY, Baruch College, Zicklin School of Business ( email )

One Bernard Baruch Way
New York, NY 10010
United States

City University of New York - Department of Law ( email )

New York, NY
United States

Yafit Lev Aretz

City University of New York - Department of Law ( email )

New York, NY
United States
9178891136 (Phone)

HOME PAGE: http://https://zicklin.baruch.cuny.edu/faculty-profile/yafit-lev-aretz/

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