When AI Fails: Who’s to Blame? Bridging the Accountability Gap in the Age of Automation Introduction Artificial Intelligence (AI) has become deeply woven into the fabric of modern life—from autonomous vehicles navigating busy streets to algorithms making decisions in healthcare, finance, and law enforcement. But what happens when these systems fail? Who bears the blame when an AI misdiagnoses a patient, causes a car accident, or makes a biased hiring decision? The growing complexity of AI systems has created a troubling accountability gap, raising urgent questions about responsibility and trust in automation. Illustration of AI algorithms and decision trees highlighting the complexity of AI decision-making This article explores the legal, ethical, and societal challenges of assigning blame when AI systems go wrong. We’ll delve into real-world case studies, examine existing legal frameworks, and discuss emerging solutions like explainable AI (XAI) and stricter regulatory oversight....