arXiv:2605.20956v1 Announce Type: new Abstract: Conformal triage converts predictive scores into deployment actions that either release a case, flag it for urgent attention, or defer it to human review. Under prevalence shift, however, the usual summaries of marginal coverage and human-review rate can miss the safety-critical question of whether patients who truly experience the target event are released without review. To address this gap, we introduce a leakage-aware deployment audit for release-side conformal triage. It first assigns target subjects to three non-overlapping roles: prevalenc
The increasing deployment of AI in critical decision-making systems necessitates robust methods for auditing their safety and reliability as these systems are integrated into more sensitive applications.
Ensuring the safety and ethical deployment of AI in sensitive areas like healthcare requires sophisticated auditing tools to prevent adverse outcomes when system conditions shift.
The introduction of leakage-aware deployment audits provides a novel method for assessing the release-side risks in AI triage systems, particularly under prevalence shift, directly addressing a critical safety gap.
- · AI ethicists
- · Healthcare sector
- · Regulatory bodies
- · AI safety researchers
- · Developers of unaudited AI systems
Increased trust in AI systems deployed in high-stakes environments due to clearer safety protocols.
Development of industry standards and regulations around AI auditing and deployment safety, potentially leading to accreditation processes.
Reduced liability for organizations deploying AI, fostering wider adoption of AI in critical infrastructure and services due to established safety nets.
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