Actionable Meta-Requirements for AI Robustness Auditing
However, designing an AI robustness auditing framework is challenging.
ProofPaper ↗
Key points
- AI-enabled systems must remain dependable when the conditions under which they operate change (e.g., due to input perturbations, distribution shifts, or changes in external dependencies).
- This makes robustness auditing of such systems important for judging whether the available evidence is sufficient to support a defined robustness conclusion.
- We address this gap through meta-requirements: higher-order requirements specifying what a class of auditing frameworks should support.
- These meta-requirements can help organizations implement AI robustness auditing frameworks, assess existing frameworks for completeness, and refine them.
Sources (1)
- [1]Actionable Meta-Requirements for AI Robustness AuditingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 10:01 PM
However, designing an AI robustness auditing framework is challenging.
AI-enabled systems must remain dependable when the conditions under which they operate change (e.g., due to input perturbations, distribution shifts, or changes in external dependencies).
Extractive summary: sentences quoted from the sources.
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