Promoting Transparency and Trust through Explainable Artificial Intelligence (XAI) in High-Stakes Domains

Abstract

Abstract: As Artificial Intelligence (AI) systems increasingly permeate high-stakes domains such as healthcare, law, and finance, the demand for transparency and accountability grows critical. Explainable AI (XAI) emerges as a vital solution, offering interpretability and justifiability for AI decisions. This paper evaluates the role of XAI in enhancing trust and understanding among end-users and stakeholders. It explores the key methods of XAI, compares their applicability in critical sectors, and discusses their impact on ethical accountability. By analyzing case studies and empirical research, this work underscores the necessity for human-centric AI designs and lays out strategies for implementing XAI effectively.

Keywords: Explainable AI, Transparency, Trust, Ethical AI, Healthcare AI, Legal AI, Accountability, Interpretability, Model Explainability, Black-box Models