How to Improve the Explanatory Power of an Intelligent Textbook: a Case Study in Legal Writing

Francesco Sovrano is a computer scientist and data-science researcher focused on explainability for responsible AI. He earned a PhD in Data Science and Computation in 2023 from the University of Bologna, in association with the Polytechnic University of Milan, developing a computational theory of explanations with applications in user interfaces, regulatory compliance, and reinforcement learning. As a postdoctoral researcher at the University of Zurich he applied explanation theories to software engineering, AI in education, and EU regulation, while also researching machine learning for code. He later became an Early-Career Fellow at ETH Zurich’s Collegium Helveticum, creating XAI tools to reveal rules and biases in LLM-generated explanations. His work aims to identify and mitigate cognitive and statistical biases in human–AI interaction, advancing transparent, ethical, and trustworthy AI.