Context-Awareness and Interpretability of Rare Occurrences for Discovery and Formalization of Critical Failure Modes

2025-04-18

AI Summary

Critical vision systems—e.g., autonomous driving perception modules—are prone to failures such as false detections, adversarial vulnerability, and hallucinations under rare or unseen scenarios, yet lack interpretable failure identification and modeling capabilities. To address this, we propose CAIRO: the first context-aware, human-judgment-incentivized, ontology-driven, knowledge-graph-based “human-in-the-loop” failure discovery framework. CAIRO formalizes black-box model failures as OWL/XML-encoded knowledge graphs, enabling shareable, logically inferable, and auditable failure representations. Technically, it integrates ontology engineering, human–machine collaborative testing, robustness analysis for object detection, and explainable AI (XAI). Evaluated on real-world autonomous driving perception systems, CAIRO significantly improves the efficiency of critical failure mode discovery, enhances semantic interpretability of failures, and enables cross-team reuse of failure models and insights.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Knowledge Representation and Reasoning: Ontologies
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Vision systems are increasingly deployed in critical domains such as surveillance, law enforcement, and transportation. However, their vulnerabilities to rare or unforeseen scenarios pose significant safety risks. To address these challenges, we introduce Context-Awareness and Interpretability of Rare Occurrences (CAIRO), an ontology-based human-assistive discovery framework for failure cases (or CP - Critical Phenomena) detection and formalization. CAIRO by design incentivizes human-in-the-loop for testing and evaluation of criticality that arises from misdetections, adversarial attacks, and hallucinations in AI black-box models. Our robust analysis of object detection model(s) failures in automated driving systems (ADS) showcases scalable and interpretable ways of formalizing the observed gaps between camera perception and real-world contexts, resulting in test cases stored as explicit knowledge graphs (in OWL/XML format) amenable for sharing, downstream analysis, logical reasoning, and accountability.

Problem

• Detect and formalize rare failure cases in vision systems
• Address vulnerabilities in AI models to rare scenarios
• Improve interpretability and sharing of failure test cases

Innovation

• Ontology-based framework for rare failure detection
• Human-in-the-loop testing for AI criticality evaluation
• Knowledge graphs for interpretable failure formalization