Explainable AI in High-Stakes Decision Making: Building Transparency, Accountability and Public Trust

Eric von Hippel

Explainable AI in High-Stakes Decision Making: Building Transparency, Accountability and Public Trust

Author(s)	Eric von Hippel
Country	United States
Abstract	Artificial Intelligence (AI) is increasingly being deployed in high-stakes environments such as healthcare, financial services, criminal justice, employment, insurance, public administration, and critical infrastructure. While AI systems can improve prediction, efficiency, and decision consistency, their use in consequential settings raises concerns regarding opacity, bias, accountability, privacy, and public trust. Explainable Artificial Intelligence (XAI) has emerged as an important approach for addressing these concerns by providing information about how AI systems generate predictions or recommendations. This study examines the role of explainable AI in high-stakes decision making, with particular emphasis on transparency, accountability, and public trust. A qualitative and conceptual methodology based on secondary literature is adopted to analyse different approaches to explainability, including interpretable models, feature attribution, counterfactual explanations, example-based explanations, and human-in-the-loop oversight. The study proposes an integrated framework in which technical explainability is combined with procedural accountability, institutional governance, human oversight, and stakeholder communication. The analysis suggests that explanations can improve understanding and contestability, but explainability alone cannot guarantee fairness or trustworthy AI. Excessively complex explanations may also create false confidence or fail to address underlying biases. Effective governance therefore requires a broader approach incorporating documentation, auditing, impact assessment, monitoring, appeal mechanisms, data governance, and clearly assigned responsibility. The study concludes that explainable AI should be understood not merely as a technical feature but as a component of responsible decision-making architecture. In high-stakes environments, meaningful transparency must enable affected stakeholders to understand, question, and appropriately challenge AI-supported decisions.
Keywords	Explainable Artificial Intelligence, High-Stakes Decision Making, Transparency, Accountability, Public Trust, AI Governance, Algorithmic Fairness, Human Oversight, Responsible AI, Artificial Intelligence.
Field	Engineering
Published In	Volume 5, Issue 4, July-August 2023
Published On	2023-07-19

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American Journal of Advanced Multidisciplinary Research and Innovation

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Explainable AI in High-Stakes Decision Making: Building Transparency, Accountability and Public Trust

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