arXiv:2606.11949v1 Announce Type: new Abstract: We present an online monitoring system for distributional shift in deployed safety classifiers, using calibrated sequential statistics to detect when a classifier has moved out of distribution. Upon detection, a conformal abstention layer adapts decision thresholds to recover a target error rate epsilon=0.1. In a pre-registered factorial evaluation (4 classifiers x 5 shift conditions x 20 seeds x 2 window sizes, 800 cells), the system achieves 86.6% valid detection (693/800, 95% CI [84.1%, 88.8%]) with mean latency of 39.5 steps. Detection holds
The increasing deployment of AI into critical 'safety-critical' systems necessitates robust monitoring and adaptation mechanisms to maintain performance and trust.
Ensuring the reliability and safety of deployed AI systems is crucial for their adoption across various industries, impacting regulatory frameworks and public acceptance.
AI systems can now be continuously monitored for performance degradation due to distributional shifts and automatically adapt, reducing the need for manual oversight and intervention.
- · AI developers
- · Safety-critical industries
- · Regulators
- · AI ethics and safety researchers
- · Companies relying on static AI deployments
- · Risk-averse traditional industries
Increased trust and accelerated adoption of AI in sensitive applications requiring high reliability.
New standards and regulatory requirements for online monitoring and adaptive AI systems may emerge, influencing AI development lifecycles.
The ability of AI systems to adapt in real-time could lead to more dynamic and resilient infrastructure, potentially creating new vulnerabilities through complex interactions.
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