Building Fair and Responsible AI: A Practical Framework for Reducing Bias

Organizations increasingly rely on AI and machine learning in cloud and enterprise environments, making fairness a critical requirement. This session explores how bias emerges across the AI lifecycle, from data collection and feature engineering to model development and deployment. Attendees will learn a practical framework for detecting, measuring, and mitigating bias using proven techniques and governance practices. The session also covers fairness-performance tradeoffs, monitoring, and compliance considerations, providing actionable guidance for building trustworthy AI systems. at scale. AI