Explainable Artificial Intelligence In High-Stakes Decision-Making: A Systematic Review Of Methods, Applications, And Challenges
Keywords:
Explainable Artificial Intelligence, High-Stakes Decision-Making, Interpretability, Transparency, Trustworthy AI, LIME, SHAP, Fairness, Responsible AI, Systematic ReviewAbstract
Explainable Artificial Intelligence (XAI) has become a vital area of research as we see more complex machine learning models being used in critical decision-making fields like healthcare, finance, criminal justice, and public policy. While these advanced AI systems often deliver impressive predictive accuracy, their black-box nature raises important questions about transparency, accountability, fairness, and trust—especially when their decisions can significantly affect people's lives and societal outcomes. This systematic review dives into the current state of explainable AI methods, their practical applications, and the hurdles we face when trying to implement XAI in crucial settings. The review brings together existing research on key explainability strategies, such as intrinsic interpretable models, post-hoc explanation techniques like LIME and SHAP, saliency-based visualization methods, and the newer counterfactual and causal explanation frameworks. Additionally, the paper showcases how XAI enhances decision support systems by boosting interpretability, aiding in bias detection, and building user trust in sensitive applications. It pays special attention to how these methods are applied in specific sectors, including clinical diagnosis, credit scoring, fraud detection, and risk assessment tools in the legal system. Despite the progress made, several challenges still need to be addressed, such as balancing model accuracy with interpretability, the subjective nature of evaluating explanations, scalability issues in real-time systems, and the potential for misleading or adversarial explanations. The review also touches on ethical, legal, and regulatory aspects, stressing the importance of standardized evaluation metrics and governance frameworks to ensure responsible AI deployment. This systematic review highlights just how crucial explainable AI is for building decision-making systems that are trustworthy, transparent, and ethically sound. It also points out future research paths that could lead to more robust, human-centered, and domain-aware solutions for explainability.
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