A new position paper argues that current methods for auditing AI systems are insufficient for identifying harms in personalized, generative AI. The authors contend that existing approaches, which often rely on static evaluations and aggregated definitions of harm, fail to capture the dynamic and user-specific nature of harm in systems that adapt over time. They propose a shift towards user- and community-centered auditing processes that acknowledge the evolving and pluralistic understanding of harm, particularly for marginalized users. AI
IMPACT Highlights the need for evolving AI auditing practices to address the unique challenges posed by personalized and adaptive generative systems.
RANK_REASON The cluster contains a single academic paper discussing AI safety and auditing methodologies. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX Code Finder for Papers
- Connected Papers
- DagsHub
- Gotit.pub
- Hugging Face
- Influence Flower
- Litmaps
- ScienceCast
- scite Smart Citations
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