Computer Science > Information Retrieval
arXiv:2207.10192 (cs)
[Submitted on 20 Jul 2022]
Title:Building Human Values into Recommender Systems: An Interdisciplinary Synthesis
Authors:Jonathan Stray,Alon Halevy,Parisa Assar,Dylan Hadfield-Menell,Craig Boutilier,Amar Ashar,Lex Beattie,Michael Ekstrand,Claire Leibowicz,Connie Moon Sehat,Sara Johansen,Lianne Kerlin,David Vickrey,Spandana Singh,Sanne Vrijenhoek,Amy Zhang,McKane Andrus,Natali Helberger,Polina Proutskova,Tanushree Mitra,Nina Vasan
View a PDF of the paper titled Building Human Values into Recommender Systems: An Interdisciplinary Synthesis, by Jonathan Stray and 20 other authors
View PDFAbstract:Recommender systems are the algorithms which select, filter, and personalize content across many of the worlds 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. It is not a comprehensive survey of this large space, but a set of highlights identified by our diverse author cohort. 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.
| Subjects: | Information Retrieval (cs.IR); Social and Information Networks (cs.SI) |
| ACM classes: | J.4; H.3.3; K.4.2 |
| Cite as: | arXiv:2207.10192 [cs.IR] |
| (orarXiv:2207.10192v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2207.10192 arXiv-issued DOI via DataCite | |
| Journal reference: | ACM Trans. Recomm. Syst. 2, 3, Article 20 (September 2024), 57 pages |
| Related DOI: | https://doi.org/10.1145/3632297 DOI(s) linking to related resources |
Full-text links:
Access Paper:
- View PDF
- Other Formats
View a PDF of the paper titled Building Human Values into Recommender Systems: An Interdisciplinary Synthesis, by Jonathan Stray and 20 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.