Ethical and Technical Challenges of Explainable AI (XAI)

Abstract

The rapid integration of artificial intelligence into critical sectors such as healthcare, finance, and criminal justice has underscored a fundamental tension between model performance and human understanding. This thesis explores the dual nature of Explainable Artificial Intelligence (XAI), analyzing the technical hurdles of post-hoc interpretability and the ethical dilemmas inherent in making complex algorithmic decisions transparent. By examining the trade-off between predictive accuracy and model simplicity, this work argues that true explainability is not merely a technical feature but a sociotechnical necessity that requires reconciling mathematical fidelity with human cognitive limitations. The discussion extends to the risks of adversarial explanations and the potential for "fair-washing," concluding that the future of XAI must balance computational rigor with robust ethical frameworks to ensure accountability in autonomous systems.