"8-10% of algorithmic recommendations are 'bad', but... an ..."

Martin Hilbert et al. (2024)

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DOI: 10.1016/J.IJINFOMGT.2023.102743

access: open

type: Journal Article

metadata version: 2024-08-04

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Martin Hilbert, Arti Thakur, Pablo M. Flores, Xiaoya Zhang, Jee Young Bhan, Patrick Bernhard, Feng Ji:
8-10% of algorithmic recommendations are 'bad', but... an exploratory risk-utility meta-analysis and its regulatory implications. Int. J. Inf. Manag. 75: 102743 (2024)

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