Explainable Artificial Intelligence for Transparent Decision-Making in High-Stakes Environments

Martin Cunneen

Explainable Artificial Intelligence for Transparent Decision-Making in High-Stakes Environments

Author(s)	Martin Cunneen
Country	United States
Abstract	Artificial intelligence is increasingly used to support decisions in healthcare, criminal justice, financial services, public administration, critical infrastructure, and emergency response. Although advanced machine-learning models can identify complex patterns, their limited interpretability creates serious concerns when predictions affect health, liberty, employment, financial security, or access to essential services. Explainable artificial intelligence seeks to address this problem by making model behavior, influential variables, uncertainty, and decision pathways more understandable to affected individuals and professional decision-makers. However, an explanation is not automatically accurate, fair, useful, or sufficient for accountability. This study develops an interdisciplinary framework for evaluating explainable artificial intelligence in high-stakes decision environments. It integrates technical fidelity, human comprehension, procedural fairness, contestability, uncertainty communication, and institutional oversight. A simulation-based comparative design is used to assess four decision-support configurations: an unexplained black-box model, a black-box model supported by local explanations, a model using combined local and global explanations, and an inherently interpretable model supported by uncertainty reporting and human review. The evaluation uses a synthetic benchmark of 2,400 decision cases distributed across healthcare, financial lending, public-benefit eligibility, and emergency-priority settings. No real patients, borrowers, or public-service applicants were involved. The simulated findings indicate that explanation-supported systems improve decision traceability, reviewer comprehension, error detection, and contestability. The most balanced performance is achieved by the inherently interpretable, human-supervised configuration, which records a composite transparency score of 88.7 compared with 41.6 for the unexplained black-box model. The study concludes that responsible explainability requires more than visual feature attribution. High-stakes systems should combine interpretable modeling, explanation validation, uncertainty disclosure, documented human oversight, bias monitoring, accessible appeal procedures, and continuing post-deployment review.
Keywords	explainable artificial intelligence, transparent decision-making, high-stakes environments, algorithmic accountability, interpretable machine learning, human oversight, contestability, responsible AI
Field	Engineering
Published In	Volume 1, Issue 1, January-February 2019
Published On	2019-01-14

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Explainable Artificial Intelligence for Transparent Decision-Making in High-Stakes Environments

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