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ERIC Number: EJ1346940
Record Type: Journal
Publication Date: 2022-Sep
Pages: 26
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1560-4292
EISSN: EISSN-1560-4306
Educating Software and AI Stakeholders about Algorithmic Fairness, Accountability, Transparency and Ethics
Bogina, Veronika; Hartman, Alan; Kuflik, Tsvi; Shulner-Tal, Avital
International Journal of Artificial Intelligence in Education, v32 n3 p808-833 Sep 2022
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.
Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://bibliotheek.ehb.be:2123/
Publication Type: Journal Articles; Reports - Descriptive
Education Level: Higher Education; Postsecondary Education; High Schools; Secondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A