Recognition: unknown
Multisided Fairness for Recommendation
read the original abstract
Recent work on machine learning has begun to consider issues of fairness. In this paper, we extend the concept of fairness to recommendation. In particular, we show that in some recommendation contexts, fairness may be a multisided concept, in which fair outcomes for multiple individuals need to be considered. Based on these considerations, we present a taxonomy of classes of fairness-aware recommender systems and suggest possible fairness-aware recommendation architectures.
This paper has not been read by Pith yet.
Forward citations
Cited by 3 Pith papers
-
Co-Designing Organizational Justice Indicators for Algorithmic Systems
Co-design workshops at Kiva show that organizational justice better captures employee concerns for recommender systems than distributional fairness alone and yields concrete monitoring metrics.
-
Offline Evaluation Measures of Fairness in Recommender Systems
The thesis identifies theoretical, empirical, and conceptual flaws in offline fairness measures for recommender systems and contributes new evaluation methods and practical guidelines.
-
Multistakeholder Impacts of Profile Portability in a Recommender Ecosystem
Data portability scenarios in algorithmic pluralism produce varying effects on user utility across different recommendation algorithms.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.