Transparency and Explainability in Responsible AI: Foundations, Challenges, and the Path Forward

Abstract

AI systems now make or heavily influence decisions about who gets a loan, who is flagged as a flight risk, and which patients receive certain treatments etc. Given these stakes, one question keeps coming up in both policy and engineering circles: do we actually understand how these systems reach their conclusions? This paper focuses on two related ideas that sit at the heart of responsible AI: transparency, meaning how open a system is about its inner workings, and explainability, meaning how well it can articulate its reasoning to the people affected by it. I survey the main technical approaches, examine why they fall short in practice, and argue that solving this problem requires more than better algorithms,it requires rethinking how AI systems are governed.

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