Fairness for niche users and providers: algorithmic choice and profile portability
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914058680139776 |
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| author | McKinnie, Elizabeth Buhayh, Anas Canel, Clement Burke, Robin |
| author_facet | McKinnie, Elizabeth Buhayh, Anas Canel, Clement Burke, Robin |
| contents | Ensuring fair outcomes for multiple stakeholders in recommender systems has been studied mostly in terms of algorithmic interventions: building new models with better fairness properties, or using reranking to improve outcomes from an existing algorithm. What has rarely been studied is structural changes in the recommendation ecosystem itself. Our work explores the fairness impact of algorithmic pluralism, the idea that the recommendation algorithm is decoupled from the platform through which users access content, enabling user choice in algorithms. Prior work using a simulation approach has shown that niche consumers and (especially) niche providers benefit from algorithmic choice. In this paper, we use simulation to explore the question of profile portability, to understand how different policies regarding the handling of user profiles interact with fairness outcomes for consumers and providers. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_22660 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fairness for niche users and providers: algorithmic choice and profile portability McKinnie, Elizabeth Buhayh, Anas Canel, Clement Burke, Robin Information Retrieval Artificial Intelligence Human-Computer Interaction Ensuring fair outcomes for multiple stakeholders in recommender systems has been studied mostly in terms of algorithmic interventions: building new models with better fairness properties, or using reranking to improve outcomes from an existing algorithm. What has rarely been studied is structural changes in the recommendation ecosystem itself. Our work explores the fairness impact of algorithmic pluralism, the idea that the recommendation algorithm is decoupled from the platform through which users access content, enabling user choice in algorithms. Prior work using a simulation approach has shown that niche consumers and (especially) niche providers benefit from algorithmic choice. In this paper, we use simulation to explore the question of profile portability, to understand how different policies regarding the handling of user profiles interact with fairness outcomes for consumers and providers. |
| title | Fairness for niche users and providers: algorithmic choice and profile portability |
| topic | Information Retrieval Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.22660 |