This session explores how explainability techniques can help mitigate ethical risks in AI systems, from biased outputs to opaque decision-making. Panelists will share practical approaches to making models more transparent, accountable, and aligned with real-world impact. Key learnings: → The role of explainability in identifying and reducing bias in model behavior → Access practical tools and techniques to increase transparency in complex systems → Debate how explainability supports trust, compliance, and responsible AI deployment