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AI Safety, Alignment & Interpretability
The latest AI xPress posts in AI Safety, Alignment & Interpretability.
- A set of natural images reduced one vision model's accuracy by about 90 percent
In a 2021 paper in the proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, researchers introduced two image datasets that reliably make machine vision…
- Score-CAM beat earlier visual explanation methods on recognition and localization tests
In a 2020 paper, researchers described Score-CAM, a method that explains why a convolutional neural network classifies an image the way it does. Convolutional neural networks are a type of artificial…
- Pixel-level interpretability model outperforms Grad-CAM on every tested metric in a COVID-19 chest X-ray study
In a 2025 paper published in Machine Learning and Knowledge Extraction, a peer-reviewed journal, researchers at Université du Québec à Chicoutimi reported that a new pixel-level interpretability…
- The machine learning model and feature collinearity highly affect SHAP and LIME explanations, a biomedical case study suggests
Researchers discussed two common explainable artificial intelligence tools, SHAP and LIME. In a case study that classified people with or without a myocardial infarction, a heart attack, the…
- Large language models repeated planted false clinical details in 50% to 82% of adversarial test cases, study reports
Large language models, the AI programs that generate text based on the information they were trained on, can produce false facts, an error researchers call hallucination. A peer-reviewed study in…
