{"id":1189039,"date":"2026-10-07T13:55:04","date_gmt":"2026-10-07T20:55:04","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/causal-quantification-of-the-sensitivity-reliability-trade-off-in-semantic-xai-comparing-object-aware-sam-and-texture-aware-slic-segmentation\/"},"modified":"2026-10-07T14:42:16","modified_gmt":"2026-10-07T21:42:16","slug":"causal-quantification-of-the-sensitivity-reliability-trade-off-in-semantic-xai-comparing-object-aware-sam-and-texture-aware-slic-segmentation","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/causal-quantification-of-the-sensitivity-reliability-trade-off-in-semantic-xai-comparing-object-aware-sam-and-texture-aware-slic-segmentation\/","title":{"rendered":"Causal Quantification of the Sensitivity-Reliability Trade-Off in Semantic XAI: Comparing Object-Aware (SAM) and Texture-Aware (SLIC) Segmentation"},"content":{"rendered":"\n\n\n<p class=\"wp-block-paragraph\">Explainable AI (XAI) methods aiming to probe model internals for scientific discovery (\u00e2\u20ac\u009d RED XAI\u00e2\u20ac\u009d) must move beyond correlational saliency maps. We address this by presenting a systematic comparison of segmentation methods within a causal attribution framework. We contrast an objectaware approach using the Segment Anything Model (SAM) against a texture-aware baseline using SLIC superpixels. Both are integrated into a pipeline utilizing Grad-CAM for saliency, CLIP for concept labeling, and a causal validation step quantifying concept importance via counterfactual interventions (blur masking) measured by raw confidence drop. Evaluating on 200 ImageNet images, we uncover a critical sensitivity-reliability trade-off: SAM-based object-centric concepts show significantly higher average causal impact (81.0% mean confidence drop vs. 37.7% for SLIC), demonstrating greater sensitivity, but suffer from segmentation failures in 9.5% of cases (181\/200 successes). SLIC achieves perfect 100% reliability (200\/200 successes) and lower impact variance, albeit with reduced sensitivity. This trade-off provides actionable guidance for domain scientists: SLIC\u00e2\u20ac\u2122s robustness is preferable for high-stakes, texture-reliant tasks (eg, medical diagnostics), while SAM\u00e2\u20ac\u2122s sensitivity may benefit exploratory analysis of object-centric phenomena. Our work offers quantitative evidence of this trade-off, enabling more informed XAI method selection for reliable scientific insight.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Explainable AI (XAI) methods aiming to probe model internals for scientific discovery (\u00e2\u20ac\u009d RED XAI\u00e2\u20ac\u009d) must move beyond correlational saliency maps. We address this by presenting a systematic comparison of segmentation methods within a causal attribution framework. We contrast an objectaware approach using the Segment Anything Model (SAM) against a texture-aware baseline using SLIC superpixels. 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