A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
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CLD integrates convex optimization and ADMM in JAX to deliver 97-98% accuracy for language detection robust to accents under low-resource conditions, with claimed theoretical stability guarantees.
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What Should Explanations Contain? A Human-Centered Explanation Content Model for Local, Post-Hoc Explanations
A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
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Convex Low-resource Accent-Robust Language Detection in Speech Recognition
CLD integrates convex optimization and ADMM in JAX to deliver 97-98% accuracy for language detection robust to accents under low-resource conditions, with claimed theoretical stability guarantees.