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Case study : predictive fairness to reduce misdemeanor r..:
, In:
Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
,
Rodolfa, Kit T.
;
Salomon, Erika
;
Haynes, Lauren
... - p. 142-153 , 2020
Link:
https://dl.acm.org/doi/10.1145/3351095.3372863
RT T1 <cTitle>Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency</cTitle>: T1 <Title>Case study : predictive fairness to reduce misdemeanor recidivism through social service interventions</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3372863&Exemplar=1&LAN=DE A1 Rodolfa, Kit T. A1 Salomon, Erika A1 Haynes, Lauren A1 Mendieta, Iván Higuera A1 Larson, Jamie A1 Ghani, Rayid PB ACM YR 2020 K1 algorithmic fairness K1 criminal justice K1 machine learning disparities K1 racial bias K1 Applied computing K1 Computers in other domains K1 Computing in government K1 Law, social and behavioral sciences K1 Computing methodologies K1 Machine learning K1 Human-centered computing K1 Interaction design K1 Social and professional topics K1 Computing/technology policy SP 142 OP 153 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3351095.3372863 DO https://dl.acm.org/doi/10.1145/3351095.3372863 SF ELIB - SuUB Bremen
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