DisaBench: A Participatory Evaluation Framework for Disability Harms in Language Models · DeepSignal
DisaBench: A Participatory Evaluation Framework for Disability Harms in Language Models arXiv cs.AI · Eugenia Kim, Ioana Tanase, Christina Mallon 3d ago · · · · DisaBench introduces a framework to evaluate disability-related harms in language models.
Key Points Developed with input from people with disabilities. Includes 12 categories of disability harms. Dataset of 175 prompts for evaluation. Reader Mode unavailable (could not extract clean content).
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Invisible orchestrators in multi-agent LLM systems pose significant safety risks and affect behavior dynamics.
📰 Read Original Signal Score
Moderate signal — interesting but narrower impact.
Weight Score
Source authority 20% 80
Community heat 20% 0
Technical impact 30%
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≥75 high · 50–74 medium · <50 low
Why Featured
DisaBench provides developers and PMs with a framework to assess and mitigate disability-related harms in language models, signaling a growing emphasis on ethical AI practices.
