Explaining Black-Box Language Models: Learning to Optimize Linguistically-Structured Word Subsets

As deep language models (DLMs) are increasingly deployed in high-stakes domains such as healthcare, understanding their decision rationale becomes paramount for ensuring trust, safety, and accountability. However, achieving this vital level of interpretability is particularly challenging when these DLMs operate as black-box systems (e.g., via APIs), where access to internal model states (e.g., parameters, gradients) is restricted. Despite numerous efforts, existing explanation methods often fail

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ethics.ai (7 June 2026), “Explaining Black-Box Language Models: Learning to Optimize Linguistically-Structured Word Subsets,” evidence record 1302, https://ethics.ai/record/1302 (originally published by arXiv).

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