Bridging the Gap: Introduction to Explainable AI (XAI)
In recent years, artificial intelligence (AI) and machine learning (ML) algorithms have achieved remarkable success across diverse domains, ranging from healthcare to finance and autonomous systems. However, the growing complexity of AI models has raised concerns regarding their transparency, accountability, and potential biases. Explainable AI (XAI) emerges as a critical solution to address these challenges by making AI systems more understandable and interpretable to humans.
Objectives:
Provide participants with a foundational understanding of the principles, importance, and challenges of Explainable AI.
Gain insights into why interpretability in AI systems is crucial
Explore various techniques for achieving explainability, and understand the ethical considerations associated with XAI.