Educating Software and AI Stakeholders About Algorithmic Fairness, Accountability, Transparency and Ethics-Reference-Cited by-同舟云学术

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

Published:2021-04-21 Issue: Volume: Page:
ISSN:1560-4292
Container-title:International Journal of Artificial Intelligence in Education
language:en
Short-container-title:Int J Artif Intell Educ

Author:

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

Funder

H2020 European Research Council

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Education

Link

https://link.springer.com/content/pdf/10.1007/s40593-021-00248-0.pdf

Reference65 articles.

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2. ACM statement on Algorithmic Transparency and accountability [Online]. Available: https://www.acm.org/binaries/content/assets/public-policy/2017_usacm_statement_algorithms.pdf. [Accessed: 02-Mar-2021].

3. AIS Global IS Education Report. (2018). Shared on EDUglopedia.org by Association for Information Systems, AIS on June 14, 2018, EID: 6149.

4. Baeza-Yates, R. (2018). Bias on the Web. Communications of the ACM, 61(6), 54–61.

5. Bellamy, R.K., Dey, K., Hind, M., Hoffman, S.C., Houde, S., Kannan, K., & Nagar, S. (2018). Ai fairness 360:, An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv preprint arXiv:1810.01943.

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