The application of artificial intelligence in some fields comes with significant constraints (including legal ones). In the case of important financial or medical recommendations, transparency in the operation of the systems is essential. The ability to provide an interpetation or explanation of the model's operation is also an important factor in building trust in artificial intelligence and user satisfaction.
During the course you will have the opportunity to learn the theoretical foundations of XAI and apply them to a project of your choice. Ideally, projects will be led by groups of 2-3 students and will involve different ML tasks and data modalities (such as tabular data, texts, images, time series). In parallel with developing XAI tools for your project, you will have the opportunity to exchange knowledge and experiences with students from other groups. This will give you the opportunity to learn about the specificity and versatility of a range of XAI methods.
