Explainable AI: The Quest for Transparency in Business and Beyond
Hanna DeSimone, Maikel Leon-Espinosa
Abstract
This paper provides an in-depth examination of Explainable Artificial Intelligence (XAI) and its significance in developing responsible AI. It covers the history of AI, the fundamental principles, the difference between explainability and interpretability, and the types of explanations. The paper highlights the role of transparency in explainable systems. Moreover, it discusses applications across high-stakes domains. Additionally, it touches on the current regulatory landscape and presents an outlook on the future of XAI research and regulation.
Topics & Concepts
Transparency (behavior)InterpretabilityComputer scienceBusiness intelligenceData scienceArtificial intelligenceManagement scienceKnowledge managementEngineeringComputer securityExplainable Artificial Intelligence (XAI)Ethics and Social Impacts of AIArtificial Intelligence in Law