KEYNOTE SPEAKERS

Abstract. Black box AI systems for automated decision-making, often based on machine learning over (big) data, map a user’s features into a class or a score without exposing the reasons why. This is problematic not only for the lack of transparency but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. The future of AI lies in enabling people to collaborate with machines to solve complex problems. Like any efficient collaboration, this requires good communication, trust, clarity and understanding. Explaining to humans how AI reasons are only a part of the problem, we must then be able to design AI systems that understand and collaborate with humans: Hybrid decision-making systems aim at leveraging the strengths of both human and machine agents to overcome the limitations that arise when either agent operates in isolation.

This lecture provides a reasoned overview to the work of Explainable AI (XAI) to date and then will focus on paradigms in support of synergistic human-machine interaction and collaboration to improve joint performance in high-stake decision-making. Three distinct paradigms, characterized by a different degree of human agency will be discussed: i) Human oversight, with a human expert monitoring AI prediction augmented with explanation; ii) Learning to defer, in which the machine learning model is given the possibility to abstain from making a prediction when it receives an instance where the risk of making a misprediction is too large; iii) Collaborative and interactive learning, in which human and AI engage in communication to integrate their distinct knowledge and facilitate the human’s ability to make informed decisions.

This lecture is a joint work with: Clara Punzi, Mattia Setzu and Roberto Pellungrini

Biography. Fosca Giannotti is professor of Computer Science at Scuola Normale Superiore, Pisa and associate at the Information Science and Technology Institute “A. Faedo” of CNR., Pisa, Italy. She co-leads the Pisa KDD Lab – Knowledge Discovery and Data Mining Laboratory, a joint research initiative of the University of Pisa and ISTI-CNR. Her research focuses on using AI and Big data to understand complex social phenomena: human mobility, social behavior, and advancing AI methods on trustworthiness and human interaction. She is author of more than 300 peer-reviewed papers. She is the PI of the ERC project “XAI – Science and Technology for the Explanation of AI Decision Making”.She has coordinated tens of European projects and industrial collaborations. Since February 2020 F.G. is the Italian Delegate of Cluster4 (Digital, Industry and Space) in Horizon Europe.

Abstract. With the inherent boom in availability of large volumes of multimodal healthcare data over the internet, their automated processing is becoming all the more relevant in today’s perspective. Manual delineation and processing is expensive, biased, and slow. Here lies the utility of artificial intelligence, encompassing machine learning with soft computing. The role of AI is in providing assistive intelligence to healthcare professionals, in their endeavour to arrive at cost-effective, fast, and viable solutions for complex decision-making.

This talk outlines the role of AI in several aspects of healthcare, encompassing classification, segmentation, and survival prediction. We discuss applications from radiomics, genomics, histopathology, with multimodal imagery spanning over X-ray, CT, MR, fundus images, to handle some diseases. The role of attention in enhancing performance is described. Finally, an outline of some fuzzy deep learning models, in the healthcare domain, is presented.

Biography. Sushmita Mitra is a senior professor at the Machine Intelligence Unit (MIU), Indian Statistical Institute, Kolkata. From 1992 to 1994 she was in the RWTH, Aachen, Germany as a DAAD Fellow. She was a Visiting Professor in the Computer Science Departments of the University of Alberta, Edmonton, Canada; Meiji University, Japan; and Aalborg University Esbjerg, Denmark. Dr. Mitra received the National Talent Search Scholarship (1978-1983) from NCERT, India, the University Gold Medal in 1988, the IEEE TNN Outstanding Paper Award in 1994 for her pioneering work in neuro-fuzzy computing, the CIMPA-INRIA-UNESCO Fellowship in 1996, and Fulbright-Nehru Senior Research Fellowship in 2018-2020. She was the INAE Chair Professor during 2018-2020. Dr. Mitra has been awarded the prestigious J. C. Bose National Fellowship, 2021.

Dr. Mitra is the author of the books “Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing” and “Data Mining: Multimedia, Soft Computing, and Bioinformatics” published by John Wiley, and “Introduction to Machine Learning and Bioinformatics”, Chapman & Hall/CRC Press, beside a host of other edited books. Dr. Mitra has guest edited special issues of several journals, is an Associate Editor of “IEEE/ACM Trans. on Computational Biology and Bioinformatics”, “Information Sciences”, “Proceedings of the INSA”, “Computers in Biology and Medicine”, and is a Founding Associate Editor of “Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery (WIRE DMKD)”. She has more than 150 research publications in referred international journals.

Dr. Mitra is a Fellow of the IEEE, The World Academy of Sciences (TWAS), Indian National Science Academy (INSA), International Association for Pattern Recognition (IAPR), Asia-Pacific Artificial Intelligence Association (AAIA), and Fellow of the Indian Academy of Sciences (IASc), Indian National Academy of Engineering (INAE), and The National Academy of Sciences, India (NASI). She serves as a Member of the Inter-Academy Panel for Women in STEMM. She has visited more than 30 countries as a Plenary/Invited Speaker or an academic visitor. She served in the capacity of General Chair, Program Chair, Tutorial Chair, of many international conferences; was the Chair, IEEE Kolkata Section (2021-2022) and an IEEE CIS Distinguished Lecturer. Her current research interests include data science, machine learning, soft computing, medical image processing, and Bioinformatics.

Abstract. The learning capability of fuzzy systems has been a long-standing research focus within the fuzzy systems community. In this talk, I will provide a comprehensive review of several successful learning paradigms applied to fuzzy systems, including supervised, unsupervised, and reinforcement learning, with applications in clustering, classification, and regression. Furthermore, I will discuss how this research can benefit from the advancements and successful experiences in neural networks. Additionally, I will highlight the importance of ensuring that learning mechanisms in fuzzy systems maintain their explainability and transparency, which are fundamental and vital characteristics of fuzzy models..

Biography. Dr. Jyh-Shing Roger Jang is the CTO of E.Sun Financial Holding in Taipei, leading AI-driven banking innovation. A professor at National Taiwan University (NTU), he previously directed NTU’s FinTech Research Center (2018–2022) and served as IT director at NTU Hospital (2017–2019). His 1993 paper on ANFIS, with over 22,000 citations, and his contributions to the Fuzzy Logic Toolbox for MATLAB earned him the 2025 IEEE CIS Fuzzy Systems Pioneer Award. His research focuses on both the theory and practice of machine learning, with applications spanning fintech, speech recognition/assessment, natural language processing, medical/healthcare data analytics, computer vision, and music analysis/retrieval.

Abstract. Knowledge graphs are one of the most popular frameworks for encoding symbolic knowledge. In applications, the knowledge that is captured by these resources is often represented using geometric constructs. Such geometric representations are easier to integrate with neural networks. Moreover, they implicitly capture some of the regularities that exist in the knowledge graph, and can thus reveal plausible knowledge that was not explicitly provided. Entities are then typically represented as high-dimensional vectors, while predicates are represented in terms of numerical scoring functions, which essentially define fuzzy relations between the entity embedding spaces. This view of predicates as fuzzy relations between embedding spaces allows us to formally characterise the kinds of regularities that are captured by a given knowledge graph embedding, and to identify limitations on the kinds of reasoning that are possible with particular embedding models. In this talk, I will given an overview of recent results in this area, and briefly highlight a number of other settings where ideas from fuzzy logic are used for reasoning about knowledge graphs.

Biography. Steven Schockaert is a professor at Cardiff University, UK. His current research focuses on Natural Language Processing, Commonsense Reasoning, Representation Learning, and Neurosymbolic Artificial Intelligence. He is editor-in-chief of the European Journal on Artificial Intelligence (formerly known as AI Communications). He is program chair of COLING 2025, an action editor for Machine Learning Journal, and a fellow of the Alan Turing Institute. He also serves on the board of SIGLEX. He has received the ECCAI Dissertation Award, the IBM Belgium Prize for Computer Science, an ACL outstanding paper award, and best paper awards at ILP and RepL4NLP, among others.