Explainable AI in Autonomous Vehicles: Safety, Accountability, and Ethics

Abstract

Autonomous Vehicles (AVs) represent one of the most safety-critical applications of Artificial Intelligence, with the potential to transform transportation systems by reducing accidents, improving mobility, and enhancing traffic efficiency. However, AVs rely heavily on complex AI models for perception, prediction, and decision-making, many of which function as opaque black boxes. This opacity raises serious ethical concerns related to safety assurance, accountability in the event of failures, and public trust. Explainable Artificial Intelligence (XAI) has emerged as a crucial enabler for addressing these challenges by providing transparency into vehicle behavior and decision logic. This paper explores the role of explainable AI in autonomous vehicles, focusing on its contributions to safety validation, legal and moral accountability, and ethical governance. It analyzes explainability techniques across AV subsystems, discusses domain-specific ethical dilemmas, and proposes a structured explainability framework for responsible AV deployment. The paper argues that explainability is not merely supportive but essential for ethically aligned autonomous mobility.

Keywords: Autonomous Vehicles, Explainable AI, Safety-Critical Systems, Accountability, Ethical AI