Interpretability, Accountability, and Algorithmic Bias in AI-Driven Healthcare: An Ethical Analysis Under the GDPR Framework

Abstract

The proliferation of artificial intelligence in healthcare has introduced transformative capabilities alongside complex ethical challenges requiring rigorous scholarly examination. This article provides a comprehensive ethical analysis of three interconnected concerns—interpretability, accountability, and algorithmic bias—within AI-driven healthcare applications, contextualized within the European General Data Protection Regulation framework. Through systematic review of contemporary literature, this study identifies critical gaps in the understanding of machine learning decision-making processes and their implications for stakeholder trust, patient safety, and regulatory compliance. Findings reveal that the opacity of AI algorithms poses significant threats to accountability, while non-representative training datasets perpetuate systemic biases that disproportionately affect marginalized populations. The GDPR provides foundational data protection mechanisms; however, its provisions require further elaboration to adequately address algorithmic transparency and fairness in clinical contexts. This article proposes a multi-layered framework combining technical explainability tools, stakeholder engagement protocols, and regulatory refinements to enhance interpretability, ensure accountability, and mitigate bias in healthcare AI applications.