Francesco Sovrano

Francesco Sovrano

Postdoc
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.
Publications
(2026). The price of precision: the cost of preprocessing for automated code revision in code review. Empir. Softw. Eng..
(2025). Detecting Semantic Clones of Unseen Functionality. 40th IEEE/ACM International Conference on Automated Software Engineering, ASE 2025, Seoul, Korea, Republic of, November 16-20, 2025.
(2025). Detecting Semantic Clones of Unseen Functionality. CoRR.
(2025). DiscoLQA: zero-shot discourse-based legal question answering on European Legislation. Artif. Intell. Law.
(2025). Explicit vs. Implicit Biographies: Evaluating and Adapting LLM Information Extraction on Wikidata-Derived Texts. CoRR.
(2025). How to Improve the Explanatory Power of an Intelligent Textbook: a Case Study in Legal Writing. Int. J. Artif. Intell. Educ..