The article discusses the importance of transparency, fairness, and accountability in AI decisions, specifically in face verification. It introduces an approach that combines human and computer vision to increase the interpretability of face verification algorithms. By leveraging Mediapipe for segmentation and model-agnostic algorithms for insights, the study aims to bridge the gap between how machines perceive faces and how humans understand them. The research focuses on explaining AI decisions by perturbing images based on semantic areas and extracting important concepts for face verification.
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by Miriam Doh (... alle arxiv.org 03-15-2024
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