Computational Linguistics for the Humanities
Instituto di Linguistica Computazionale “Antonio Zampolli” – CNR
Pisa – Italy
Abstract: The synergy between Computational Linguistics (CL) and humanities disciplines has its roots in a relatively distant past, going back to CL origins. Over the last years, we are witnessing the revitalization of this ancient synergy to which different factors have contributed, ranging from the maturity of language technologies to the increasing availability of digital texts. In this talk, I will illustrate and discuss what this renewed synergic relationship looks like today, i.e how methods, tools and resources for natural language processing can help in the analysis of texts and language in the humanities and social sciences, what challenges need to be faced in order to strengthen this synergy, and what is its impact on both scientific and applied sides. Different case studies will be presented, using language technology with the goal of finding new answers to existing research questions in a particular humanities discipline or addressing entirely new research questions.
Bio: Simonetta is Director of Research at the Institute of Computational Linguistics “Antonio Zampolli” of the Italian National Research Council (ILC-CNR). Her research spans across different areas of Computational Linguistics, with a specific interest in exploring and understanding impact and role of Natural Language Processing methods and techniques for the advancement of humanities disciplines. She taught Computational Linguistics and Digital Humanities courses in different Italian universities, is member of Scientific Committees of major conferences in the field and co-editor in chief of the “Italian Journal of Computational Linguistics” (IJCoL). Since 2013, Simonetta Montemagni is the Director of ILC-CNR.
Human-centered Artificial Intelligence
University of Pisa – Italy.
Abstract: The future of AI lies in enabling people to collaborate with machines to tackle complex problems with enhanced capabilities. Like any efficient collaboration, this requires good communication, trust, clarity, and understanding. This also reveals a social dimension of AI, as increasingly complex socio-technical systems emerge, made by interacting people and intelligent agents at different scales, from individuals to communities and societies. The lecture will address the individual and social dimensions of human-AI collaboration with a focus on: i) “Explainable AI”, a brief introduction to the research challenges and state of the art in XAI systems for better decision making, and ii) the study of emerging network effects and collective outcomes of social AI systems and the design of transparent mechanisms for human-AI collaboration that may help achieve desired aggregate outcomes in reference to agreed set of values and objectives at collective levels, such as accessible urban mobility, diversity, pluralism, fair distribution of economic resources, and environmental sustainability.
Bio: Dino Pedreschi is a professor of computer science at the University of Pisa, and a pioneering scientist in data science and artificial intelligence. He co-leads the Pisa KDD Lab – Knowledge Discovery and Data Mining Laboratory, a joint research initiative of the University of Pisa, Scuola Normale Superiore and the Italian National Research Council – CNR. He is currently shaping the research frontier of Human-centered Artificial Intelligence, as a leading figure in the European network of research labs Humane-AI-Net (scientific director of the line “Social AI”) and as the coordinator of the Spoke project “Human-centered AI” of the Next Generation EU national program FAIR – Future AI Research. He is a founder of SoBigData.eu, the European H2020 Research Infrastructure “Big Data Analytics and Social Mining Ecosystem”. He is the coordinator of the Italian National PhD program in Artificial Intelligence. He is a designated expert of GPAI, the Global Partnership on AI – Responsible AI Working Group, since 2020.
He obtained in 1987 a PhD in computer science from the University of Pisa.
His research focus is on big data analytics and mining, machine learning and AI, and their impact on society: human mobility and sustainable cities, social network analysis, complex social and economic systems, data and AI ethics, discrimination-preventing and privacy-preserving data analytics, explainable AI, synergistic human-AI collaboration and co-evolution.
- XAI (2019-2025, ERC Advanced Grants 2018, co-PI) Science and technology for the explanation of AI decision making.
- SoBigData (2015-2024, H2020-Excellent Science Research Infrastructures) Integrated Infrastructure for Social Mining & Big Data Analytics. A research infrastructure for open data science for social good, at the second stage of “Advanced community”, aggregating 32 partners of 12 EU Countries.
- Humane-AI-Net (2019-2024, H2020-ICT-48, European networks of AI excellence centres) Toward AI Systems That Augment and Empower Humans by Understanding Us, our Society and the World Around Us.
- FAIR (2023-2026, Next Generation EU – PNRR Project) Future Artificial Intelligence Research. Spoke 1 – “Human-centered AI”.