Designing an Intervention Tool for End-User Algorithm Audits in Personalized Recommendation Systems
<|reference_start|>Designing an Intervention Tool for End-User Algorithm Audits in Personalized Recommendation Systems: 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.<|reference_end|>
@article{wu2024designing,
    title={Designing an Intervention Tool for End-User Algorithm Audits in
  Personalized Recommendation Systems},
    author={Qunfang Wu, Lu Xian},
    journal={arXiv preprint arXiv:2409.13176},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13176},
    primaryClass={cs.HC}
}