Explainable Artificial Intelligence (XAI) in High-Stakes Applications
DOI:
https://doi.org/10.15662/IJEETR.2025.0704001Keywords:
Explainable Artificial Intelligence, XAI, high-stakes applications, interpretability, healthcare AI, financial AI, trust in AI, ethical AI, post-hoc explanationsAbstract
Explainable Artificial Intelligence (XAI) has emerged as a critical area of research aimed at enhancing transparency, trust, and accountability in AI systems, especially within high-stakes applications. These applications— such as healthcare, finance, autonomous driving, and criminal justice—often involve significant consequences, where erroneous or opaque AI decisions can lead to severe harm. The challenge lies in balancing the high predictive performance of complex AI models with the need for interpretability and user understanding. This paper explores the current state of XAI methods tailored for high-stakes domains, emphasizing their importance in fostering user trust and ethical AI deployment. Through a comprehensive literature review, we categorize the predominant XAI techniques, including posthoc explanation models, inherently interpretable models, and hybrid approaches, evaluating their suitability in different scenarios. We then present a research methodology that applies selected XAI frameworks to real-world datasets from healthcare and finance, assessing both model explain ability and decision accuracy. Our findings reveal that while inherently interpretable models provide clearer explanations, they sometimes sacrifice predictive power. Conversely, complex models paired with post-hoc explanations offer robust performance but risk misleading or incomplete interpretations. The discussion highlights critical trade-offs and proposes evaluation metrics that consider both explanation quality and decision impact. The paper concludes by identifying gaps in current XAI approaches, particularly the need for standardized explanation evaluation in high-stakes contexts and the integration of user-centric design principles. Future work aims to develop adaptive XAI models that dynamically tailor explanations based on stakeholder expertise and application criticality. This research underscores the necessity of explain ability in ensuring responsible AI use where human lives, finances, and justice are at stake.ax
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