From Ethics to Implementation: Shaping the Future of AI Governance

Abstract

With AI systems increasingly influencing critical sectors, ensuring responsible governance is paramount. This talk dives into the technical process of converting ethical AI principles into enforceable governance frameworks. I’ll outline how to integrate bias mitigation techniques, transparency mechanisms, and accountability structures within machine learning workflows. Specifically, the session will cover the use of fairness metrics, model interpretability methods like SHAP and LIME, and automated tools for ensuring model compliance with legal and ethical standards throughout the development and post-deployment phases. Drawing from real-world examples in finance and healthcare, I will demonstrate how ethical AI policies can be operationalized, with a focus on monitoring models for fairness, enhancing auditability with explainable AI (XAI) tools, and aligning systems with industry regulations. Attendees will leave with practical strategies for engaging cross-functional teams—from data scientists to legal and compliance teams—in building robust AI governance frameworks that ensure trust, accountability, and compliance.