Transparency and Explainable AI: Bridging Privacy, Fairness, and Accountability
Transparency and Explainable AI: Bridging Privacy, Fairness, and Accountability

Summary/Abstract: This paper examines whether Explainable Artificial Intelligence (XAI) can address AI ethics, specifically, privacy, fairness, and accountability, by linking technical transparency with interpretability to facilitate ethical oversight. It combines review and normative analysis from multiple fields. Three case studies - COMPAS, Uber, and Clearview AI - illustrate XAI's role in revealing biases, tracing accountability, and identifying privacy risks. We argue that transparency alone is insufficient; understanding is hindered by overload, misinterpretation, and lack of context. Effective explainability requires coupling transparency with interpretability within legal, social, and organizational frameworks. XAI highlights ethical issues but needs human judgment and safeguards. The paper connects XAI to ethical principles, demonstrating that explainability is necessary but insufficient, and emphasizes the need for interdisciplinary and regulatory collaboration, such as the EU AI Act, to guide the development of responsible AI.

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