COMET: A coordinated multi-layer runtime monitoring framework for human-supervised AI-enabled safety systems

Key points

• Incorporating AI systems into safety systems requires monitoring to avoid silent faults and ensure safe and reliable operation.1
• In this work, COMET consists of three monitoring layers: signal integrity, physics consistency, and the AI system's competence envelope.1
• The framework is evaluated on an open dataset of compressed-air leaks, where an AI system detects leaks from acoustic emissions, with held-out conditions simulating covariate shift, semantic shift, and sensor faults, events expected during runtime but unknown in their exact appearance.1
• Runtime monitoring frameworks for AI-enabled safety systems must therefore combine quantitative fault detection and isolation with qualitative, human-oriented feedback.1

Sources (1)

• [1]COMET: A coordinated multi-layer runtime monitoring framework for human-supervised AI-enabled safety systems
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 02:40 PM

  We present COMET, a coordinated multi-layer runtime monitoring framework for human-supervised AI-enabled safety systems in which each monitor is anchored in a different fault mode.

  Incorporating AI systems into safety systems requires monitoring to avoid silent faults and ensure safe and reliable operation.

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