Explainable Artificial Intelligence (XAI), 3 hp

Artificial Intelligence is increasingly playing an integral role in our daily activities. Moreover, AI based solutions are used more and more in areas such as criminal justice, healthcare, and education, and therefore, their impact is high. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias, and a demand for model transparency and interpretability.

Why did the system make this prediction? Do I trust it? What would happen if I change some parameter?

As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand AI models.

In this course, we present an introduction to Explainable Artificial Intelligence (XAI). We describe the challenges associated with the use of black-box models and how we can overcome such challenges using interpretable and explainable methods. Moreover, we study other aspects related to interacting with AI-based systems, for example, trust, acceptance, evaluation, and Fairness Accountability and Transparency-issues.

The course is given in English and is targeted for working professionals in industry.