Educating Software and AI Stakeholders About Algorithmic Fairness, Accountability, Transparency and Ethics

Veronika Bogina, Alan Hartman, Tsvi Kuflik, Avital Shulner-Tal

Research output: Contribution to journalArticlepeer-review

Abstract

This paper discusses educating stakeholders of algorithmic systems (systems that apply Artificial Intelligence/Machine learning algorithms) in the areas of algorithmic fairness, accountability, transparency and ethics (FATE). We begin by establishing the need for such education and identifying the intended consumers of educational materials on the topic. We discuss the topics of greatest concern and in need of educational resources; we also survey the existing materials and past experiences in such education, noting the scarcity of suitable material on aspects of fairness in particular. We use an example of a college admission platform to illustrate our ideas. We conclude with recommendations for further work in the area and report on the first steps taken towards achieving this goal in the framework of an academic graduate seminar course, a graduate summer school, an embedded lecture in a software engineering course, and a workshop for high school teachers.

Original languageEnglish
Pages (from-to)808-833
Number of pages26
JournalInternational Journal of Artificial Intelligence in Education
Volume32
Issue number3
DOIs
StatePublished - Sep 2022

Keywords

  • Accountability
  • Algorithmic literacy
  • Education
  • Fairness
  • Transparency

All Science Journal Classification (ASJC) codes

  • Education
  • Computational Theory and Mathematics

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