Found in Translation: semantic approaches for enhancing AI interpretability in face verification

• URL: http://arxiv.org/abs/2501.05471v1
• Date: Mon, 06 Jan 2025 08:34:53 GMT
• Title: Found in Translation: semantic approaches for enhancing AI interpretability in face verification
• Authors: Miriam Doh, Caroline Mazini Rodrigues, N. Boutry, L. Najman, Matei Mancas, Bernard Gosselin,
• Abstract summary: This study extends previous work by integrating semantic concepts into XAI frameworks to bridge the comprehension gap between model outputs and human understanding. We propose a novel approach combining global and local explanations, using semantic features defined by user-selected facial landmarks. Results indicate that our semantic-based approach, particularly the most detailed set, offers a more nuanced understanding of model decisions than traditional methods.
• Score: 0.4222205362654437
• License:
• Abstract: The increasing complexity of machine learning models in computer vision, particularly in face verification, requires the development of explainable artificial intelligence (XAI) to enhance interpretability and transparency. This study extends previous work by integrating semantic concepts derived from human cognitive processes into XAI frameworks to bridge the comprehension gap between model outputs and human understanding. We propose a novel approach combining global and local explanations, using semantic features defined by user-selected facial landmarks to generate similarity maps and textual explanations via large language models (LLMs). The methodology was validated through quantitative experiments and user feedback, demonstrating improved interpretability. Results indicate that our semantic-based approach, particularly the most detailed set, offers a more nuanced understanding of model decisions than traditional methods. User studies highlight a preference for our semantic explanations over traditional pixelbased heatmaps, emphasizing the benefits of human-centric interpretability in AI. This work contributes to the ongoing efforts to create XAI frameworks that align AI models behaviour with human cognitive processes, fostering trust and acceptance in critical applications.

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