The Role of Explainable AI in Building Public Trust: A Study of AI-Driven Public Policy Decisions
Abstract
The rapid advancement of Artificial Intelligence (AI) in various sectors has led to its significant adoption in public policy decision-making. While AI-driven systems have demonstrated efficiency and scalability, the lack of transparency in their decision-making processes has raised concerns about public trust. Explainable AI (XAI) emerges as a promising solution to address these concerns by offering interpretable and understandable models. This research paper examines the role of XAI in fostering public trust, focusing on its application in AI-driven public policy decisions. The study explores the theoretical foundations of XAI, emphasizing its importance in addressing issues of fairness, accountability, and transparency. Through real-world case studies in healthcare and urban planning, the paper illustrates how XAI methods like SHAP and LIME have enhanced decision-making processes and public trust. Furthermore, the research identifies technical and ethical challenges in implementing XAI, including model complexity and stakeholder resistance. By combining qualitative analysis of case studies with quantitative public perception surveys, the study provides actionable recommendations to promote XAI adoption. These include policy frameworks, technical advancements, and collaborative efforts among stakeholders. Ultimately, the paper argues that XAI is pivotal for bridging the gap between technological advancements and societal acceptance, paving the way for responsible AI integration in public policy.
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