Abstract
In the communities of UMAP, RecSys, and similar venues, fairness of recommender systems has primarily been addressed from the perspective of computer science and artificial intelligence, e.g., by devising computational bias and fairness metrics or elaborating debiasing algorithms. In contrast, we advocate taking a multiperspective and multidisciplinary viewpoint to complement this technical perspective. This involves considering the variety of stakeholders in the value chain of recommender systems as well as interweaving expertise from various disciplines, in particular, computer science, law, ethics, sociology, and psychology (e.g., studying discrepancies between computational metrics of bias and fairness and their actual human perception, and considering the legal and regulatory context recommender systems are embedded in).
Originalsprache | englisch |
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Titel | UMAP2022 - Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization |
Herausgeber (Verlag) | Association of Computing Machinery |
Seiten | 90-94 |
Seitenumfang | 5 |
ISBN (elektronisch) | 9781450392327 |
DOIs | |
Publikationsstatus | Veröffentlicht - 4 Juli 2022 |
Veranstaltung | 30th ACM Conference on User Modeling, Adaptation and Personalization: UMAP 2022 - Virtual, Online, Spanien Dauer: 4 Juli 2022 → 7 Juli 2022 |
Konferenz
Konferenz | 30th ACM Conference on User Modeling, Adaptation and Personalization |
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Kurztitel | UMAP2022 |
Land/Gebiet | Spanien |
Ort | Virtual, Online |
Zeitraum | 4/07/22 → 7/07/22 |
ASJC Scopus subject areas
- Software