Operationalizing Ethical AI

This paper proposes an empirically grounded framework for evaluating the ethical performance of AI-based decision support systems in high stakes institutional contexts. Drawing on sociotechnical systems theory, it reframes AI ethics as observable human–AI interaction patterns. Ethical risks such as accountability gaps, bias, over-reliance and responsibility diffusion are operationalized through measurable indicators derived from simulations and human-in-the-loop experiments. The framework enables continuous, data-driven assessment of ethical performance and supports adaptive governance in real AI deployments.