From Algorithms to Accountability: The Societal and Ethical Need for Explainable AI

As artificial intelligence (AI) becomes increasingly integrated into critical sectors such as healthcare, finance, and law, the opacity of black-box models raises profound ethical, societal, and regulatory concerns. This paper investigates the transition from black-box AI to explainable white-box systems, focusing on ethical imperatives, societal trust, and regulatory compliance. Through a comprehensive literature review and qualitative interviews with industry professionals, this study highlights the challenges and opportunities of implementing Explainable AI (XAI) in high-stakes contexts. We emphasize the importance of transparency, accountability, and fairness in AI and propose a roadmap for the successful adoption of XAI, advocating for interdisciplinary collaboration between technologists, policymakers, and civil society to ensure responsible AI deployment.