As AI systems become more integrated into critical sectors, such as healthcare, finance, and law enforcement, ensuring that these systems are both reliable and transparent is of paramount importance. Explainable AI (XAI) focuses on developing methods to make AI decision-making more understandable and interpretable to humans, helping build trust and accountability.

Key topics include:

• Understanding AI Decisions: How can we design AI models that not only perform well but also explain their reasoning? This part of the session will explore techniques that allow AI systems to provide human-readable explanations of their decisions.

• Transparency in AI Systems: Discussing how transparency in AI algorithms fosters trust and acceptance, especially in high-stakes applications like autonomous vehicles or medical diagnosis.

• Trustworthy AI: Exploring the balance between model accuracy and interpretability. How can we ensure that the need for complex, high-performing models doesn't compromise our ability to understand their actions?

• Tools and Techniques for XAI: Reviewing the latest advancements in tools and frameworks designed to make AI more interpretable, such as decision trees, LIME, SHAP, and counterfactual explanations.

• Human-AI Interaction: Understanding how users engage with AI explanations and how explainability affects decision-making, adoption, and trust in AI systems.

• Ethical and Legal Considerations: Exploring the legal and ethical dimensions of explainability, particularly in scenarios where AI decisions have a direct impact on human lives, such as criminal justice or healthcare.

This session will offer researchers, engineers, and practitioners an in-depth understanding of how Explainable AI can ensure more accountable, transparent, and trustworthy AI systems, enabling safer, more ethical applications across industries.