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.

Bibliographical note

Funding Information:
This research has been partly supported by the CyCAT, which has received funding from the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement No. 810105. The Barcelona workshop was supported by funding from the SeeRRI project under Grant Agreement No. 824588.

Publisher Copyright:
© 2021, International Artificial Intelligence in Education Society.

Keywords

  • Accountability
  • Algorithmic literacy
  • Education
  • Fairness
  • Transparency

ASJC Scopus subject areas

  • Education
  • Computational Theory and Mathematics

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