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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2409.13176 |
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| _version_ | 1866910613967470592 |
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| author | Wu, Qunfang Xian, Lu |
| author_facet | Wu, Qunfang Xian, Lu |
| contents | As algorithms increasingly shape user experiences on personalized recommendation platforms, there is a growing need for tools that empower end users to audit these algorithms for potential bias and harms. This paper introduces a novel intervention tool, MapMyFeed, designed to support everyday user audits. The tool addresses key challenges associated with user-driven algorithm audits, such as low algorithm literacy, unstructured audit paths, and the presence of noise. MapMyFeed assists users by offering guiding prompts, tracking audit paths via a browser extension, and visualizing audit results through a live dashboard. The tool will not only foster users' algorithmic literacy and awareness but also enhance more transparent and fair recommendation systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13176 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Designing an Intervention Tool for End-User Algorithm Audits in Personalized Recommendation Systems Wu, Qunfang Xian, Lu Human-Computer Interaction As algorithms increasingly shape user experiences on personalized recommendation platforms, there is a growing need for tools that empower end users to audit these algorithms for potential bias and harms. This paper introduces a novel intervention tool, MapMyFeed, designed to support everyday user audits. The tool addresses key challenges associated with user-driven algorithm audits, such as low algorithm literacy, unstructured audit paths, and the presence of noise. MapMyFeed assists users by offering guiding prompts, tracking audit paths via a browser extension, and visualizing audit results through a live dashboard. The tool will not only foster users' algorithmic literacy and awareness but also enhance more transparent and fair recommendation systems. |
| title | Designing an Intervention Tool for End-User Algorithm Audits in Personalized Recommendation Systems |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2409.13176 |