Explainable AI (XAI)

Moving away from the "black box" nature of neural networks, the XAI sub-category focuses on model interpretability. We discuss research and techniques for opening the internal logic of AI systems and forcing them to explain their decisions in human-readable terms. This is critical for trust in high-stakes fields like medicine and law. Join researchers who are using saliency maps, layer-wise relevance propagation, and symbolic bridges to make artificial intelligence transparent, accountable, and understandable.