Learning Algorithms and Discrimination

Nizan Geslevich Packin, Yafit Lev-Aretz

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


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 applies 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.
Original languageEnglish
Title of host publicationResearch Handbook on the Law of Artificial Intelligence
EditorsWoodrow Barfield, Ugo Pagallo
PublisherEdward Elgar Publishing Ltd.
Number of pages26
ISBN (Electronic)9781786439055
ISBN (Print)9781786439048
StatePublished - 28 Dec 2018
Externally publishedYes

ASJC Scopus subject areas

  • General Social Sciences
  • General Computer Science


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