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1 Ergebnisse
1
8% - 10% of algorithmic recommendations are 'bad', but… an ..:
Hilbert, Martin
;
Thakur, Arti
;
Ji, Feng
...
qt3g8458d0. , 2023
Link:
https://escholarship.org/uc/item/3g8458d0
RT Journal T1
8% - 10% of algorithmic recommendations are 'bad', but… an exploratory risk-utility meta-analysis and its regulatory implications
UL https://suche.suub.uni-bremen.de/peid=base-ftcdlib:oai:escholarship.org:ark:_13030_qt3g8458d0&Exemplar=1&LAN=DE A1 Hilbert, Martin A1 Thakur, Arti A1 Ji, Feng A1 Flores, Pablo M A1 Zhang, Xiaoya A1 Bhan, Jee Young A1 Bernhard, Patrick PB eScholarship, University of California YR 2023 K1 recommender algorithms K1 algorithmic auditing K1 machine behavior K1 meta-analysis K1 digital harms JF qt3g8458d0 LK http://dx.doi.org/https://escholarship.org/uc/item/3g8458d0 DO https://escholarship.org/uc/item/3g8458d0 SF ELIB - SuUB Bremen
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