Journal article
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis
ACM Transactions on Recommender Systems
13 Nov 2023
Abstract
Recommender systems are the algorithms which select, filter, and personalize content across many of the world's largest platforms and apps. As such, their positive and negative effects on individuals and on societies have been extensively theorized and studied. Our overarching question is how to ensure that recommender systems enact the values of the individuals and societies that they serve. Addressing this question in a principled fashion requires technical knowledge of recommender design and operation, and also critically depends on insights from diverse fields including social science, ethics, economics, psychology, policy and law. This paper is a multidisciplinary effort to synthesize theory and practice from different perspectives, with the goal of providing a shared language, articulating current design approaches, and identifying open problems. We collect a set of values that seem most relevant to recommender systems operating across different domains, then examine them from the perspectives of current industry practice, measurement, product design, and policy approaches. Important open problems include multi-stakeholder processes for defining values and resolving trade-offs, better values-driven measurements, recommender controls that people use, non-behavioral algorithmic feedback, optimization for long-term outcomes, causal inference of recommender effects, academic-industry research collaborations, and interdisciplinary policy-making.
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Details
- Title
- Building Human Values into Recommender Systems: An Interdisciplinary Synthesis
- Creators
- Jonathan Stray - University of California, BerkeleyAlon Halevy - Meta AI, USAParisa Assar - Adobe Systems (United States)Dylan Hadfield-Menell - Department of Electrical Engineering and Computer Science, MIT, USACraig Boutilier - Google (United States)Amar Ashar - Photon Spot (United States)Chloe Bakalar - Meta Inc., USALex Beattie - Photon Spot (United States)Michael Ekstrand - Drexel UniversityClaire Leibowicz - Partnership on AI, USAConnie Moon Sehat - Hacks/Hackers, USASara Johansen - Stanford UniversityLianne Kerlin - British Broadcasting Corporation (United Kingdom)David Vickrey - Meta Inc., USASpandana Singh - Meta Inc., USASanne Vrijenhoek - University of AmsterdamAmy Zhang - University of WashingtonMckane Andrus - Partnership on AI, USANatali Helberger - University of AmsterdamPolina Proutskova - British Broadcasting Corporation (United Kingdom)Tanushree Mitra - University of WashingtonNina Vasan - Stanford University
- Publication Details
- ACM Transactions on Recommender Systems
- Publisher
- ACM
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Other Identifier
- 991021818387304721