Explainable Artificial Intelligence for Transparent and Trustworthy Decision-Making

Nancy Pollard

Explainable Artificial Intelligence for Transparent and Trustworthy Decision-Making

Author(s)	Nancy Pollard
Country	United States
Abstract	Artificial Intelligence (AI) is increasingly being integrated into decision-making processes across healthcare, finance, education, criminal justice, employment, public administration, and business. While AI systems can improve efficiency, prediction, and analytical capabilities, the growing use of complex machine-learning models has created concerns regarding transparency, accountability, fairness, and trust. Explainable Artificial Intelligence (XAI) has emerged as an important approach for addressing these concerns by providing mechanisms through which AI-generated predictions and decisions can be interpreted by users and stakeholders. This study examines the role of XAI in developing transparent and trustworthy decision-making systems. A qualitative and conceptual methodology is adopted through an analysis of existing research on explainability, interpretable machine learning, algorithmic accountability, and responsible AI. The study examines major XAI techniques, including feature importance, local and global explanations, surrogate models, counterfactual explanations, and model-specific interpretation approaches. It further analyses the relationship between explainability, trust, fairness, human oversight, and organizational accountability. The findings indicate that explainability can improve users' understanding of AI outputs, facilitate error detection, support regulatory compliance, and strengthen human-AI collaboration. However, explanation quality depends on the context, user expertise, model complexity, and purpose for which the explanation is required. Excessive or poorly designed explanations may create confusion or false confidence. The study proposes a human-centred XAI framework based on transparency, interpretability, fairness, accountability, security, and continuous evaluation. It concludes that explainability should not be treated as a technical feature alone but as an essential component of responsible and trustworthy AI governance.
Keywords	Explainable Artificial Intelligence, XAI, Transparent AI, Trustworthy AI, Machine Learning, Algorithmic Accountability, Interpretability, AI Governance, Human-AI Interaction, Responsible AI.
Field	Engineering
Published In	Volume 4, Issue 6, November-December 2022
Published On	2022-11-04

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American Journal of Advanced Multidisciplinary Research and Innovation

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Explainable Artificial Intelligence for Transparent and Trustworthy Decision-Making

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