An argument paper reframes LLM explainability as an embodied, situated practice based on Dourish and enactivist cognition, identifying ontological obstacles in internal explanations and advocating affordance-based designs.
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6 Pith papers cite this work. Polarity classification is still indexing.
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The authors provide a detailed taxonomy of 21 risks associated with language models, covering discrimination, information leaks, misinformation, malicious applications, interaction harms, and societal impacts like job loss and environmental costs.
Compares LIME, input perturbation and attention for explaining QA on KB+text; proposes automatic evaluation paradigm and finds input perturbation superior in both automatic and human studies.
Position paper proposing Model Science as a discipline to systematically analyze AI model behavior beyond benchmarks, drawing analogies from cognitive science, neuroscience, medicine, and agriculture.
A structured review of JSP 936 identifies eight challenge areas in operationalising AI assurance for UK Defence and concludes that further methods, guidance, and organisational capability are required.