8–10% of algorithmic recommendations are ‘bad’, but… an exploratory risk-utility meta-analysis and its regulatory implications

Author:

Hilbert Martin,Thakur Arti,Flores Pablo M.,Zhang Xiaoya,Bhan Jee Young,Bernhard Patrick,Ji Feng

Funder

University of California Davis

Publisher

Elsevier BV

Subject

Library and Information Sciences,Computer Networks and Communications,Information Systems

Reference143 articles.

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4. Albadi, N., Kurdi, M., & Mishra, S. (2022). Deradicalizing YouTube: Characterization, Detection, and Personalization of Religiously Intolerant Arabic Videos. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2), 505:1–505:25. https://doi.org/10.1145/3555618.

5. Technologically scaffolded atypical cognition: The case of YouTube’s recommender system;Alfano;Synthese,2021

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