Explaining Black-Box Language Models: Learning to Optimize Linguistically-Structured Word Subsets
arXiv:2606.08497v1 Announce Type: new Abstract: 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 to concurrently satisfy three key desiderata: (i) i
As AI models become more pervasive and powerful, particularly in sensitive domains, the urgency for transparent and understandable decision-making increases, driving research into interpretability methods for black-box systems.
Understanding the rationale behind AI decisions is critical for trust, safety, and accountability, especially as model deployment extends into high-stakes applications, affecting regulatory compliance and public acceptance.
The development of robust methods for explaining black-box language models can lead to more auditable and trustworthy AI systems, potentially accelerating their adoption in highly regulated sectors where interpretability is a prerequisite.
- · AI ethicists
- · Healthcare sector (AI adoption)
- · Regulatory bodies
- · Companies developing interpretability tools
- · Developers of opaque AI systems
- · Sectors reliant on unexplainable AI
- · Companies with poor AI governance
Increased interpretability allows for better debugging and validation of AI models, reducing deployment risks.
Greater trust in AI systems could accelerate their integration into critical infrastructure and decision-making processes, leading to new economic efficiencies and ethical challenges.
The ability to audit black-box models may become a standard regulatory requirement, influencing future AI development cycles and fostering a more responsible AI ecosystem.
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Read at arXiv cs.AI