Equitable algorithms : examining ways to reduce AI bias in financial services : hearing before the Task Force on Artificial Intelligence of the Committee on Financial Services, U.S. House of Representatives, One Hundred Sixteenth Congress, second session, February 12, 2020.
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Author
United States. Congress. House. Committee on Financial Services. Task Force on Artificial Intelligence,
Published
Washington : U.S. Government Publishing Office, 2021.
Physical Desc
1 online resource (v, 121 pages) : illustrations
Status
Unavailable/Withdrawn

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Links

https://purl.fdlp.gov/GPO/gpo151859

Subjects

LC Subjects
Artificial intelligence.
Computer algorithms.
Consumer protection -- United States.
Financial engineering -- United States.
Financial services industry -- United States.

More Details

Published
Washington : U.S. Government Publishing Office, 2021.
Format
Unknown
Language
English
UPC
42-821

Notes

General Note
Access ID (govinfo): CHRG-116hhrg42821.
General Note
"Serial no. 116-87."
Bibliography
Includes bibliographical references.
Participants/Performers
Hearing witnesses: Ghani, Rayid, Distinguished Career Professor, Machine Learning Department and the Heinz College of Information Systems and Public Policy, Carnegie Mellon University; Henry-Nickie, Makada, David M. Rubenstein Fellow, Governance Studies, Race, Prosperity, and Inclusion Initiative, Brookings Institution; Kearns, Michael, Professor and National Center Chair, Department of Computer and Information Science, University of Pennsylvania; Thomas, Philip S., Assistant Professor and Co-Director of the Autonomous Learning Lab, College of Information and Computer Sciences, University of Massachusetts Amherst; Williams, Bari A., Attorney and Emerging Tech AI & Privacy Advisor.
Date/Time and Place of Event
Date of hearing: 2020-02-12.

Citations

APA Citation, 7th Edition (style guide)

United States. Congress. House. Committee on Financial Services. Task Force on Artificial Intelligence. (2021). Equitable algorithms: examining ways to reduce AI bias in financial services : hearing before the Task Force on Artificial Intelligence of the Committee on Financial Services, U.S. House of Representatives, One Hundred Sixteenth Congress, second session, February 12, 2020 . U.S. Government Publishing Office.

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

United States. Congress. House. Committee on Financial Services. Task Force on Artificial Intelligence. 2021. Equitable Algorithms: Examining Ways to Reduce AI Bias in Financial Services : Hearing Before the Task Force On Artificial Intelligence of the Committee On Financial Services, U.S. House of Representatives, One Hundred Sixteenth Congress, Second Session, February 12, 2020. U.S. Government Publishing Office.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

United States. Congress. House. Committee on Financial Services. Task Force on Artificial Intelligence. Equitable Algorithms: Examining Ways to Reduce AI Bias in Financial Services : Hearing Before the Task Force On Artificial Intelligence of the Committee On Financial Services, U.S. House of Representatives, One Hundred Sixteenth Congress, Second Session, February 12, 2020 U.S. Government Publishing Office, 2021.

MLA Citation, 9th Edition (style guide)

United States. Congress. House. Committee on Financial Services. Task Force on Artificial Intelligence. Equitable Algorithms: Examining Ways to Reduce AI Bias in Financial Services : Hearing Before the Task Force On Artificial Intelligence of the Committee On Financial Services, U.S. House of Representatives, One Hundred Sixteenth Congress, Second Session, February 12, 2020 U.S. Government Publishing Office, 2021.

Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.

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