When Confidence Lacks Concepts: Interpretable OOD Detection via Representation Perturbations
arXiv:2606.16196v1 Announce Type: new Abstract: Deep neural networks have achieved remarkable performance across medical imaging tasks, yet their tendency to overgeneralize under distributional shifts poses a major obstacle to safe clinical deployment. Out-of-Distribution (OOD) detection methods aim to mitigate this risk, but most existing approaches rely on opaque internal signals with poorly understood semantic meaning, limiting trust in safety-critical settings. In this work, we propose an interpretable OOD detection framework that probes the stability of model predictions under class-condi
The rapid deployment of AI in safety-critical applications like medical imaging necessitates robust methods for identifying out-of-distribution data to prevent catastrophic failures.
Improved OOD detection in AI, especially with interpretable methods, enhances trust and safety, accelerating the adoption of AI in regulated and high-stakes domains.
The ability to understand 'why' an AI flags data as OOD, rather than just 'that' it is OOD, changes how models are audited, validated, and deployed in sensitive areas.
- · Healthcare AI developers
- · Regulatory bodies
- · Patients
- · AI safety researchers
- · Developers of uninterpretable black-box AI systems
- · Organizations relying solely on opaque OOD detection
Increased reliability and broader acceptance of AI in medical diagnosis and treatment planning.
Reduced incidence of medical errors attributable to AI misinterpretation of novel or rare conditions.
Potential for new regulatory frameworks specifically mandating interpretable OOD detection for critical AI applications.
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Read at arXiv cs.LG