arXiv:2606.09046v1 Announce Type: new Abstract: Useful audits reveal not only how often a model fails, but also where its failures concentrate. An auditor may test many candidate explanations: long inputs, indirect questions, distracting evidence, or combinations of these factors. The risk is selection. The largest observed effect may reflect a real failure mode, or it may simply be the best result among many tried. We introduce Janus, a procedure for deciding when a proposed error explanation is credible enough to report. The goal is not to generate new explanations, but to decide which ones
As Language Models become more pervasive and critical, rigorous methods for identifying and explaining their failure modes are essential for responsible deployment and trust.
This work introduces a concrete procedure to rigorously audit and validate explanations for AI failures, moving beyond anecdotal observations to statistically sound conclusions.
The ability to systematically and credibly identify why an AI model fails shifts from qualitative observation to a more quantitative, evidence-based process, enabling more targeted improvements.
- · AI developers
- · AI auditors
- · Organizations deploying AI
- · Responsible AI initiatives
- · AI models with unexplainable failures
- · Organizations relying on superficial AI evaluations
Systematic identification of language model failure modes accelerates model improvement and robustness.
Increased trust in AI systems due to more transparent and auditable failure analysis could accelerate AI adoption in sensitive domains.
Standardization of failure audit methodologies could lead to regulatory requirements for AI explainability and auditability.
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Read at arXiv cs.LG