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Fairness metrics have become a useful tool to measure how fair or unfair a machine learning system may be for its stakeholders. In the context of recommender systems, previous research has explored how various stakeholders experience algorithmic fairness or unfairness, but it is also important to capture these experiences in the design of fairness metrics. Therefore, we conducted four focus groups with providers (those whose items, content, or profiles are being recommended) of two different domains: content creators and dating app users. We explored how our participants experience unfairness on their associated platforms, and worked with them to co-design fairness goals, definitions, and metrics that might capture these experiences. This work represents an important step towards designing fairness metrics with the stakeholders who will be impacted by their operationalizations. We analyze the efficacy and challenges of enacting these metrics in practice and explore how future work might benefit from this methodology.
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The Many Faces of Fairness: Exploring the Institutional Logics of Multistakeholder Microlending Recommendation
Recommender systems have a variety of stakeholders. Applying concepts of fairness in such systems requires attention to stakeholders’ complex and often-conflicting needs. Since fairness is socially constructed, there are numerous definitions, both in the social science and machine learning literatures. Still, it is rare for machine learning researchers to develop their metrics in close consideration of their social context. More often, standard definitions are adopted and assumed to be applicable across contexts and stakeholders. Our research starts with a recommendation context and then seeks to understand the breadth of the fairness considerations of associated stakeholders. In this paper, we report on the results of a semi-structured interview study with 23 employees who work for the Kiva microlending platform. We characterize the many different ways in which they enact and strive toward fairness for microlending recommendations in their own work, uncover the ways in which these different enactments of fairness are in tension with each other, and identify how stakeholders are differentially prioritized. Finally, we reflect on the implications of this study for future research and for the design of multistakeholder recommender systems.
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- PAR ID:
- 10434420
- Date Published:
- Journal Name:
- FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency
- Page Range / eLocation ID:
- 1652 to 1663
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3593013.3594106
