Bridging Human Concepts and Computer Vision for Explainable Face Verification
With Artificial Intelligence (AI) influencing the decision-making process of sensitive applications such as Face Verification, it is fundamental to ensure the transparency, fairness, and accountability of decisions. Although Explainable Artificial Intelligence (XAI) techniques exist to clarify AI decisions, it is equally important to provide interpretability of these decisions to humans. In this paper, we present an approach to combine computer and human vision to increase the explanation's interpretability of a face verification algorithm. In particular, we are inspired by the human perceptual process to understand how machines perceive face's human-semantic areas during face comparison tasks. We use Mediapipe, which provides a segmentation technique that identifies distinct human-semantic facial regions, enabling the machine's perception analysis. Additionally, we adapted two model-agnostic algorithms to provide human-interpretable insights into the decision-making processes.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Face VerificationFairnessSimilar Papers 제목 키워드 기반
Entropy-based Logic Explanations of Neural Networks
Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains. Concept-based neural networks have arisen as explainable-by-design methods …
Explainable artificial intelligenceImage ClassificationQuantifying Visual Image Quality: A Bayesian View
Image quality assessment (IQA) models aim to establish a quantitative relationship between visual images and their perceptual quality by human observers. IQA modeling plays a special bridging role between vision science …
Image Quality AssessmentComputer Vision Models Show Human-Like Sensitivity to Geometric and Topological Concepts
With the rapid improvement of machine learning (ML) models, cognitive scientists are increasingly asking about their alignment with how humans think. Here, we ask this question for computer vision models and human sensit…
Odd One OutSensitivityConcept Complement Bottleneck Model for Interpretable Medical Image Diagnosis
Models based on human-understandable concepts have received extensive attention to improve model interpretability for trustworthy artificial intelligence in the field of medical image analysis. These methods can provide …
DiagnosticExplainable ModelsMedical Image AnalysisTrustworthy Conceptual Explanations for Neural Networks in Robot Decision-Making
Black box neural networks are an indispensable part of modern robots. Nevertheless, deploying such high-stakes systems in real-world scenarios poses significant challenges when the stakeholders, such as engineers and leg…
AttributeDecision MakingDiagnostic