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Closing the AI accountability gap : defining an end-to-e..:
, In:
Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
,
Raji, Inioluwa Deborah
;
Smart, Andrew
;
White, Rebecca N.
... - p. 33-44 , 2020
Link:
https://dl.acm.org/doi/10.1145/3351095.3372873
RT T1
Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
: T1
Closing the AI accountability gap : defining an end-to-end framework for internal algorithmic auditing
UL https://suche.suub.uni-bremen.de/peid=acm-3372873&Exemplar=1&LAN=DE A1 Raji, Inioluwa Deborah A1 Smart, Andrew A1 White, Rebecca N. A1 Mitchell, Margaret A1 Gebru, Timnit A1 Hutchinson, Ben A1 Smith-Loud, Jamila A1 Theron, Daniel A1 Barnes, Parker PB ACM YR 2020 K1 accountability K1 algorithmic audits K1 machine learning K1 responsible innovation K1 Social and professional topics K1 Professional topics K1 Management of computing and information systems K1 System management K1 Software and its engineering K1 Software creation and management K1 Software development process management K1 Technology audits SP 33 OP 44 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3351095.3372873 DO https://dl.acm.org/doi/10.1145/3351095.3372873 SF ELIB - SuUB Bremen
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