Authors: Ms. Roshni Shailesh Gupta
Abstract: Machine learning (ML) models are being widely adopted in high-stakes sectors such as healthcare and finance due to their ability to uncover patterns in data and produce predictive insights. However, many of these models function as opaque "black boxes," making it difficult for end-users and stakeholders to understand how specific decisions are derived. This lack of interpretability can erode trust, hinder adoption, and raise ethical and regulatory concerns, particularly when decisions affect individuals' health or financial well-being. Explainable Machine Learning (XML) aims to mitigate these issues by introducing methods that make ML models more transparent and understandable. This paper presents a comprehensive examination of XML techniques, evaluates their implementation across healthcare and finance, and proposes a methodological framework to enhance both accuracy and interpretability in ML systems. The findings highlight that XML is not merely a technical enhancement but a critical enabler of trustworthy, fair, and responsible artificial intelligence (AI).
