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XAI Auditing for Pedestrian Detection Under Domain Shift Explored

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the faithfulness of explainable AI (XAI) methods when applied to pedestrian detection models under varying driving conditions. The study audits a YOLOv8s model across different datasets, revealing that explanation faithfulness is strongly correlated with detection strength. This suggests that simple confidence-based comparisons can be unreliable, and controlling for detection strength is crucial for accurate XAI auditing in safety-critical applications like autonomous vehicles. AI

IMPACT Highlights the need for robust XAI auditing methods to ensure AI safety in critical applications like autonomous driving.

RANK_REASON Research paper published on arXiv detailing a new method for auditing AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

XAI Auditing for Pedestrian Detection Under Domain Shift Explored

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Research paper published on arXiv detailing a new method for auditing AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ruben Dario Florez-Zela ·

    Confidence-Controlled XAI Auditing for Pedestrian Detection under Domain Shift

    arXiv:2610.02364v1 Announce Type: new Abstract: Explainability is increasingly required for perception models in intelligent vehicles, yet whether explanations remain faithful under driving domain shift is still poorly understood. This work audits post-hoc explanations of a fixed…

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