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Explainable AI (XAI) has become an essential component
of AI-driven systems used in high-stakes decision-making, such as
healthcare, finance, criminal justice, and autonomous systems. The
increasing reliance on AI for critical tasks necessitates transparency
and interpretability to ensure ethical, fair, and accountable decision
making. A lack of explainability in AI models can lead to biased
outcomes, regulatory non-compliance, and diminished user trust,
particularly in sensitive applications where lives and livelihoods are at
stake. This research explores the necessity of XAI in high-risk
applications, evaluates key interpretability techniques, discusses
challenges in implementation, and outlines future directions in the
f
ield. Various approaches to explainability, such as feature importance
analysis, rule-based models, counterfactual explanations, and model
simplification, are examined in detail to highlight their effectiveness in
different domains. Additionally, the paper addresses critical
challenges, including the trade-off between accuracy and
interpretability, computational complexity, and regulatory constraints,
which hinder the widespread adoption of XAI.
Keywords:
Cite Article:
"explainable AI for high stake decission making", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.c378-c383, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504265.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator