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30 Jun-5 Jul 2024 YOKOHAMA (Japan)
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ForgtAI Workshop July 2 - Room: 211+212 Final Program 8:20-10:00 First session (100 min)
14:20-16:20 Second session (2 hours)
16:40-18:40 Third session (2 hours)
Establishing and upholding trust in AI systems is an imperative pursuit as Machine Learning becomes intricately interwoven into our daily lives. The workshop, "Forging Trust in Artificial Intelligence" brings together a group of experts and researchers from diverse subfields, converging on the exploration of how transparency, fairness, privacy, and security collectively contribute to making machine learning trustworthy. By uniting experts across these pivotal disciplines, this workshop illuminates the best practices that not only enhance the trustworthiness of AI but also reinforce its ethical foundations. Ensuring trust in machine learning is necessary for unlocking its potential while minimizing risks. This is especially true in the current environment, where the constant expansion of data sources aligns with a growing interest in using them to develop comprehensive and universally applicable AI systems. This interest highlights the need to address issues related to transparency, fairness, privacy and security, particularly in the area of multimodal learning, where various data types and learners are combined to create sophisticated, but often opaque AI systems. Within this context, establishing best practices for data integration is essential to ensure transparency and interpretability of AI systems based on diverse learners. Fairness considerations, on the other hand, may involve identifying and addressing potential biases from different modalities. This includes exploring approaches to mitigate their impact and leveraging fair representation learning when integrating information from sources with varying bias levels. By addressing such issues alongside data privacy and security concerns, this workshop aims to contribute to the development of ethical, transparent, and secure AI that has a positive impact on our global society’s well-being. The following list includes (but is not limited to) relevant topics that will be addressed within this workshop: Interpretability in ML and NLP Models
Privacy and Security
Fairness and Ethical Development
Organizers Name: Dr. Corina Besliu Affiliation: Department of Software Engineering and Automation,Technical University of Moldova Web Page: https://www.linkedin.com/in/corina-besliu-phd-5929884b/ Brief Biographie: Corina Besliu earned her PhD in Economics from the University of Utah, specializing in Econometrics and Statistics. She transitioned from academia to the US corporate sector, where she served as an ML scientist before co-founding Bevian LLC. As a co-founder, Corina actively contributes to the ML and AI business community, providing customized AI solutions to companies and guiding them toward automation and data-driven strategies. Simultaneously, she holds a part-time position as a university lecturer at the Technical University of Moldova, where she teaches courses in Artificial Intelligence, Machine Learning, and Data Mining and supervises students’ master's in their thesis writing. Her current focus involves the integration and fine-tuning of Large Language Models (LLMs) for diverse business contexts. Research-wise, she explores various aspects of natural language processing, including domain-specific NLP, multimodal NLP, privacy-preserving NLP, and explainable AI in NLP. Dedicated to bridging academia and industry, Corina routinely organizes guest lectures from industry experts and presides over hackathon juries in an effort to foster collaboration and knowledge exchange between students and industry professionals.
Name: Pr. Dimitris Kotzinos Affiliation: CY Cergy Paris University Web Page: https://www.etis-lab.fr/2022/01/17/kotzinos-dimitrios/ Brief Biographie: Dimitris Kotzinos is a Professor at the Department of Computer Science of the CY Cergy Paris University, member of the ETIS Lab and member of the MIDI team of the lab. He holds a Ph.D. in Computer Science (in Real-Time Web Information Systems) (2001) and a M.Sc. in Transportation (1996), where he studied networks and their applications in transportation systems. His main research interests include data management algorithms, techniques and tools; development of methodologies, algorithms and tools for web-based information systems, portals and web services; and the understanding of the meaning (semantics) of interoperable data and services on the web and also the understanding and summarization of large Knowledge Graphs (KGs). He is working on studying the formation and evolution of discussions in online social networks using Machine Learning (ML) and Artificial Intelligence (AI) techniques, as well as the evolution of social graphs. Additionally, he is also working in the area of accountability, explainability and fairness of the ML and AI algorithms, especially when applied in data engineering and analysis problems; this includes issues on data privacy and especially their intersection with the publication of Linked Open Data. This work extends also in the area of personal mobility and personal social networks. Dimitris has published more than 70 articles in various journals, books, conferences and workshops and serves as a program committee member and reviewer for various conferences and journals. He is also participating in nationally and internationally funded research programs around data analytics, data models and networks and their integration in the everyday life.
Name: Pr. Nistor Grozavu Affiliation: CY Cergy Paris University Web Page: www.grozavu.fr Google Scholar Page: https://scholar.google.com/citations?user=0zGAcwgAAAAJ&hl=fr Brief Biographie: Nistor Grozavu received his HdR degree from Sorbonne Paris Nord University in 2020 and his PhD in Unsupervised Machine Learning from Paris 13 University in 2009. He is currently a full professor in Computer Science at CY Cergy Paris University. His research is with the MIDI team of ETIS Laboratory. His research interests include multimodal clustering, multimodal representation learning, unsupervised learning, transfer learning, dimensionality reduction, collaborative learning, machine learning by matrix factorization and content-based information retrieval, quantum machine learning. These researches are applied in different applications for text mining, visual information retrieval, recommendation systems, fraud detection, etc. via ANR, FUI, PEPS CNRS, AUF projects. He is also a member of IEEE, INNS and co-founder of the INNS Autonomous Machine Learning group. Nistor is co-author of a patent on visual information retrieval, has published two book chapters, 12 peer-reviewed journal papers, and more than 40 international conference papers. Nistor Grozavu co-supervised 2 postdocs, 8 PhD students, and supervises 1-2 Master interns each year.
Name: Pr. Seiichi Ozawa Affiliation: Graduate School of Engineering, Kobe University Web Page: http://www2.kobe-u.ac.jp/~ozawasei/ Google Scholar Page: https://scholar.google.com/citations?user=k4jRbH0AAAAJ&hl=en Brief Biographie: Seiichi Ozawa received Dr. Eng. in computer science from Kobe University. He is currently the deputy director of The Center for Mathematical and Data Sciences along with a full professor with Department of Electrical and Electronic Engineering, Graduate School of Engineering and The Center for Advanced Medical Engineering Research & Development, Kobe University, Japan. His current research interests are machine learning, incremental learning, big data analytics, cybersecurity, text mining, computer vision, and privacy-preserving machine learning. He published more than 160 journal and conference papers, and book chapters/monographs. He is currently an associate editor of IEEE Trans. on Neural Networks and Learning Systems, IEEE Trans. on Cybernetics and 2 international journals. He is the Vice-President for Membership of International Neural Network Society, the President of Asia Pacific Neural Network Society, and the Vice-President of Japan Neural Network Society. He is a member of Neural Networks TC and Smart World TC of IEEE CI Society.
Name: Dr. Nicoleta Rogovschi Affiliation: University of Paris Web Page: www.rogovschi.fr Google Scholar Page: https://scholar.google.com/citations?user=BmXsQG0AAAAJ&hl=fr Brief Biographie: Nicoleta Rogovschi received her Master's degree in Computer Science from the University Paris 13 in 2006 in the field of Machine Learning. She is currently an Associate Professor of Computer Science at Paris Descartes University. She obtained her Ph.D. in Computer Science (Probabilistic Unsupervised Learning) in 2009 at the Computer Science Laboratory of Paris 13 University and the HdR (ability to direct research) in 2021 at the Sorbonne Paris Nord University. She is a member of the Data Mining (GFD) team. Her research interests include probabilistic multi-modal learning, unsupervised learning, clustering and co-clustering methods for different types of data in different contexts: anonymization, recommender systems, opinion detection,... She is also a member of EGC, AFIA, IEEE, INNS, INNS AML group. Nicoleta Rogovschi has supervised 5 PhD students and tens of Master Research students.
Name: Dr. Aikaterini Tzompanaki Affiliation: CY Cergy Paris University Web Page: https://perso-etis.ensea.fr/tzompanaki/ Google Scholar Page: https://scholar.google.fr/citations?user=3NqU8V8AAAAJ&hl=fr Brief Biographie: Dr. Aikaterini Tzompanaki is an Associate Professor at CY Cergy Paris University, affiliated with the ETIS laboratory. Her research focuses on the explainability of data processes and machine learning algorithms, as well as data privacy. She has published her work in top-tier international conferences such as VLDB, CIKM, EDBT, PAKDD, ISWC, among others. She regularly participates in program committees for conferences, serves on juries, and acts as a reviewer for international journals. Contact Corina Besliu : corina.besliu@ati.utm.md Nistor Grozavu : nistor.grozavu@cyu.fr |
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