Impossible Explanations?
Ronan Hamon, H. Junklewitz, Gianclaudio Malgieri, Paul De Hert, Laurent Beslay, Ignacio Sanchez
Abstract
Can we achieve an adequate level of explanation for complex machine learning models in high-risk AI applications when applying the EU data protection framework? In this article, we address this question, analysing from a multidisciplinary point of view the connection between existing legal requirements for the explainability of AI systems and the current state of the art in the field of explainable AI.
Topics & Concepts
Multidisciplinary approachComputer scienceField (mathematics)Point (geometry)Connection (principal bundle)State (computer science)Artificial intelligenceData scienceRisk analysis (engineering)EngineeringProgramming languagePolitical scienceMathematicsLawGeometryPure mathematicsStructural engineeringMedicineExplainable Artificial Intelligence (XAI)Adversarial Robustness in Machine LearningArtificial Intelligence in Healthcare and Education