Machine learning models are becoming increasingly complex and powerful, but they can also be difficult to understand. Explainable AI (XAI) is a field of research that seeks to develop techniques for making machine learning models more interpretable. This is important for a number of reasons, including:

Enhancing trust and transparency: By understanding how a model makes its predictions, users can be more confident in its decisions.
Debugging and improving models: XAI techniques can be used to identify and fix problems with machine learning models.
Exploring new applications: XAI can help researchers and developers to explore new applications for machine learning, such as in healthcare, finance, and security.
There are a variety of XAI techniques available, each with its own strengths and weaknesses. Some of the most common techniques include:

Local interpretability: This approach explains the prediction for a single data point by examining the model's decision making process at that point.
Global interpretability: This approach explains the overall behavior of the model by looking at how it responds to different types of data.
Counterfactual explanation: This approach explains why the model made a particular prediction by showing how the prediction would have changed if one or more of the input features had been different.
In this talk, we will discuss the different XAI techniques and how they can be used to interpret and understand machine learning models. We will also discuss the challenges of XAI and the future directions of research in this field.

This talk is intended for developers, data scientists, and researchers who are interested in learning more about XAI. No prior knowledge of machine learning is required.