Designing Human-Centered AI Systems: Ethics, Transparency, and Accountability

Abstract

Artificial intelligence is rapidly changing the landscape of decision making in critical areas, but questions of fairness, transparency, and accountability are at the top of ethical AI discussion. This empirical research paper examines the design of human-centred AI systems by analyzing the COMPAS recidivism risk assessment tool, which is popular in the criminal justice system of the U.S. Building on secondary qualitative analysis of the publicly accessible COMPAS dataset, the study reveals the way in which algorithmic predictions, even though statistically validated, demonstrate significant differences between racial groups. Black defendants are much more likely to be misclassified as high risk compared to the white defendants and this raises serious questions about bias and equity. The research shows that technical accuracy in itself, even when the COMPAS manages to obtain moderate predictive validity (AUC ≈ 0.70), is not enough for ethical deployment. In its place, the findings promote the incorporation of dynamic fairness constraints, clear explanations, and strong accountability mechanisms. This work offers practical insights for policymakers, AI developers, and practitioners to inform them of how to build more just and trustworthy AI systems that are consistent with societal values and human rights.