User Perception of Fairness-Calibrated Recommendations
Authors: 
Gabrielle Alves, 
Dietmar Jannach, Rodrigo Ferrari De Souza, 
Marcelo Garcia ManzatoAuthors Info & Claims
Gabrielle Alves, Dietmar Jannach, Rodrigo Ferrari De Souza, Marcelo Garcia ManzatoAuthors Info & ClaimsPages 78 - 88
Abstract
The research community has become increasingly aware of possible undesired effects of algorithmic biases in recommender systems. One common bias in such systems is to over-proportionally expose certain items to users, which may ultimately result in a system that is considered unfair to individual stakeholders. From a technical perspective, calibration approaches are commonly adopted in such situations to ensure that the individual user’s preferences are better taken into account, thereby also leading to a more balanced exposure of items overall. Given the known limitations of today’s predominant offline evaluation approaches, our work aims to contribute to a better understanding of the users’ perception of the fairness and quality of recommendations when these are served in a calibrated way. Therefore, we conducted an online user study (N=500) in which we exposed the treatment groups with recommendations calibrated for fairness in terms of two different item characteristics. Our results show that calibration can indeed be effective in guiding the users’ choices towards the “fairness items” without negatively impacting the overall quality perception of the system. We however also found that calibration did not measurably impact the users’ fairness perceptions unless explanatory information is provided by the system. Finally, our study points to challenges when applying calibration approaches in practice in terms of finding appropriate parameters.
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Index Terms
- User Perception of Fairness-Calibrated Recommendations
Recommendations
Calibrated recommendations
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Putting Users in Control of their Recommendations
RecSys '15: Proceedings of the 9th ACM Conference on Recommender SystemsThe essence of a recommender system is that it can recommend items personalized to the preferences of an individual user. But typically users are given no explicit control over this personalization, and are instead left guessing about how their actions ...
Calibrated Recommendations as a Minimum-Cost Flow Problem
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June 2024
338 pages
ISBN:9798400704338
DOI:10.1145/3627043
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Published: 22 June 2024
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July 1 - 4, 2024
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