AI RESEARCH
When Can We Trust Deep Neural Networks? Towards Reliable Industrial Deployment with an Interpretability Guide
arXiv CS.CV
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ArXi:2604.19206v1 Announce Type: new The deployment of AI systems in safety-critical domains, such as industrial defect inspection, autonomous driving, and medical diagnosis, is severely hampered by their lack of reliability. A single undetected erroneous prediction can lead to catastrophic outcomes. Unfortunately, there is often no alternative but to place trust in the outputs of a trained AI system, which operates without an internal safeguard to flag unreliable predictions, even in cases of high accuracy.