Self-Explainability
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Related Articles from SNS
Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions
arXiv:2606.09568v1 Announce Type: new Abstract: The growing complexity of self-adaptive and self-organising systems, fuelled by advances in Artificial Intelligence (AI), has made them increasingly difficult to understand and trust. While Explainable AI aims to provide insight into AI decision-making, a more advanced goal is for systems to explain themselves - an ability referred to as Self-Explainability (SX).
TTE-CAM: Self-Explainable Class Activation Maps for Pretrained Black-Box CNNs
Announce Type: replace Abstract: Convolutional neural networks (CNNs) achieve state-of-the-art performance in medical image analysis yet remain opaque, limiting adoption in high-stakes clinical settings. Existing approaches face a fundamental trade-off: post-hoc methods provide unfaithful approximate explanations, while inherently interpretable architectures are faithful but often sacrifice predictive performance.
Why 'psychopath' is a dangerous label when it comes to criminal justice
Why 'psychopath' is a dangerous label when it comes to criminal justice Robert Egan Associate Editor A defendant stands in the dock. An expert describes them as a "psychopath." In an instant, one word threatens to eclipse their history, circumstances and the crime itself.