Laura State, PhD
Laura State is a Postdoctoral researcher in the Impact AI project. Through the development of a transdisciplinary auditing methodology, the project aims to evaluate the contribution of artificial intelligence (AI) systems towards societal transformation and ecological sustainability.
Her research focuses on understanding the implications of AI on society and the planet, and on developing methods to assess these implications. The overall aim is to better understand how AI can be used to build a more sustainable future. Her research has a strong interdisciplinary character, drawing on her education in the hard sciences and experience both in academia and industry.
Laura’s PhD thesis focuses on transparency and accountability issues of AI, specifically non-interpretable machine learning models. Key contributions include interdisciplinary work on explanations and EU regulation, as well as the development of the explanation tool REASONX.
She obtained her PhD in Data Science from the Scuola Normale Superiore in Pisa, Italy and was advised by Salvatore Ruggieri and Franco Turini. Next to her role as a PhD student, she was also an Early Stage Researcher in the Marie Skłodowska-Curie ITN NoBIAS, based at the University of Pisa.
Laura State holds a MSc in Neural Information processing from the University of Tübingen, and a BSc in Physics from the University of Rostock.
AIImpact AI: Evaluating the impact of AI for sustainability and public interest
The research project Impact AI: Evaluating the social impact of AI systems for sustainability and the sublic interest seeks to address these questions by...
Journal articles and conference proceedings
State, L., & Bringas Colmenarejo, A., & Beretta, A., & Ruggieri, S., & Turini, F., & Law, S. (2025). The explanation dialogues: an expert focus study to understand requirements towards explanations within the GDPR. Artificial Intelligence and Law. DOI: https://doi.org/10.1007/s10506-024-09430-w Publication details
Bringas Colmenarejo, A., & State, L., & Comandé, G. (2025). How should an explanation be? A mapping of technical and legal desiderata of explanations for machine learning models. International Review of Law, Computers & Technology. DOI: https://doi.org/10.1080/13600869.2025.2497633 Publication details
Alvarez, J. M., & Bringas Colmenarejo, A., & Elobaid, A., & Fabrizzi, S., & Fahimi, M., & Ferrara, A., & Ghodsi, S., & Mougan, C., & Papageorgiou, I., & Reyero, P., & Russo, M., & Scott, K. M., & State, L., & Zhao, X., & Ruggieri, S. (2024). Policy advice and best practices on bias and fairness in AI. Ethics and Information Technology, 26(article number 31). DOI: https://doi.org/10.1007/s10676-024-09746-w Publication details
Lampridis, O., & State, L., & Guidotti, R., & Ruggieri, S. (2023). Explaining short text classification with diverse synthetic exemplars and counter-exemplars. Machine Learning, 112, 4289-4322. DOI: https://doi.org/10.1007/s10994-022-06150-7 Publication details
