Bias and Discrimination in AI: a cross-disciplinary perspective

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)
327 Downloads (Pure)

Abstract

With the widespread and pervasive use of Artificial Intelligence (AI) for automated decision-making systems, AI bias is becoming more apparent and problematic. One of its negative consequences is discrimination: the unfair, or unequal treatment of individuals based on certain characteristics. However, the relationship between bias and discrimination is not always clear. In this paper, we survey relevant literature about bias and discrimination in AI from an interdisciplinary perspective that embeds technical, legal, social and ethical dimensions. We show that finding solutions to bias and discrimination in AI requires robust cross-disciplinary collaborations.
Original languageEnglish
Pages (from-to)72
Number of pages80
JournalIEEE TECHNOLOGY AND SOCIETY MAGAZINE
Volume40
Issue number2
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • Fairness
  • Bias
  • Discrimination

Fingerprint

Dive into the research topics of 'Bias and Discrimination in AI: a cross-disciplinary perspective'. Together they form a unique fingerprint.

Cite this