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

Authors :
Bogina, Veronika
Hartman, Alan
Kuflik, Tsvi
Shulner-Tal, Avital
Source :
International Journal of Artificial Intelligence in Education. Sep 2022 32(3):808-833.
Publication Year :
2022

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.

Details

Language :
English
ISSN :
1560-4292 and 1560-4306
Volume :
32
Issue :
3
Database :
ERIC
Journal :
International Journal of Artificial Intelligence in Education
Publication Type :
Academic Journal
Accession number :
EJ1346940
Document Type :
Journal Articles<br />Reports - Descriptive
Full Text :
https://doi.org/10.1007/s40593-021-00248-0