Building Explainable AI for Critical Data Science Applications

Nivedhaa N

Authors

Nivedhaa N B.Tech, AI & Data Science, Rajalakshmi Institute of Technology, Chennai, India Author

Keywords:

Explainable AI (XAI), Interpretable Machine Learning, High-Stakes Applications, Data Science, Model Transparency, Healthcare AI, Financial AI, Model Interpretability

Abstract

Explainable and interpretable artificial intelligence (XAI) has gained significant attention, particularly in high-stakes data science applications where decision-making transparency is crucial. This paper provides an overview of the current landscape of XAI, emphasizing its importance in domains such as healthcare, finance, and criminal justice, where outcomes can significantly impact individuals' lives. Through a comprehensive literature review, we examine the techniques and challenges in achieving model transparency and how various sectors address these concerns. Our analysis includes a case study on healthcare to demonstrate the trade-offs between model accuracy and interpretability. We present a comparative evaluation of existing XAI methods and propose recommendations for future research aimed at improving interpretability without compromising performance. The findings underscore the necessity of explainability in high-stakes applications, suggesting that tailored approaches are needed for specific domains.

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