Tutorial

Responsible AI

In the first part of the tutorial, to set the stage, we cover irresponsible AI: (1) discrimination (e.g., facial recognition, justice); (2) phrenology (e.g., biometric based predictions); (3) limitations (e.g., human incompetence, minimal adversarial AI), (4) indiscriminate use of computing resources (e.g., large language models) and (5) the impact of generative AI (disinformation, mental health and copyright issues). These examples do have a personal bias but set the context for the second part where we address three challenges: (1) principles & governance, (2) regulation and (3) our cognitive biases. We finish discussing our responsible AI initiatives and the near future.

Bio

Ricardo Baeza-Yates is Director of Research at the Institute for Experiential AI of Northeastern University. Before, he was VP of Research at Yahoo Labs, based in Barcelona, Spain, and later in Sunnyvale, California, from 2006 to 2016. He is co-author of the best-seller Modern Information Retrieval textbook published by Addison-Wesley in 1999 and 2011 (2nd ed), that won the ASIST 2012 Book of the Year award. From 2002 to 2004 he was elected to the Board of Governors of the IEEE Computer Society and between 2012 and 2016 was elected for the ACM Council. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow, among other awards and distinctions. He obtained a Ph.D. in CS from the University of Waterloo, Canada, in 1989, and his areas of expertise are web search and data mining, information retrieval, bias on AI, data science and algorithms in general.

Tutorial

Analysis of Graph Neural Networks via Graph Query Languages

Graph neural networks (GNNs) are the predominant architectures for a variety of learning tasks on graphs. A major line of research in graph learning is to understand what GNNs can and cannot do: their expressive power. In this course we explain how to understand the behaviour of GNNs by embedding them in a query language and then analyzing the query language. We will focus on probabilistic analysis of GNNs: how do the models behave on large random graphs? There are many different flavors of GNNs, and also a wide variety of different random graph model. In the tutorial, we will overview several variations of each, since they impact the analysis.

At the center of the course will be convergence results for GNNs, saying that on a random input, the GNN does something very simple. These results can be related to the limitations of standard GNN models, and can motivated extensions. But we will also relate them to a long line of research in probabilistic analysis of logics and query languages, a line going back to work of Ron Fagin in 1976. The talk will include joint work with Sam Adam-Day, Ismail Ceylan, and Ben Finkeshtein (Almost Surely Asymptotically Constant Graph Neural Networks). It will also include ongoing joint work with Sam Adam-Day and Alberto Larrauri.

Bio

Michael Benedikt is a professor at Oxford University’s computer science department, and a fellow of University College Oxford. He came to Oxford after a decade in US industrial research laboratories, including a position as Distinguished Member of Technical Staff at Bell Laboratories. He has worked extensively in computational logic, finite model theory, verification, and data management, and specializes in the interaction between these topics. He has been a keynote in past meetings on mathematical logic, computational logic, description logics, and databases. He has co-authored papers receiving best paper awards and test-of-time awards in major conferences within databases and theoretical computer science, and he has served as the program chair of both the ACM Principles of Database Systems conference (2012) and the International Conference on Database Theory (2017). He currently holds an Established Career Fellowship from the UK’s Engineering and Physical Science’s Research Council, and serves on the steering committees for the Association for Symbolic Logic, the European Association for Theoretical Computer Science, and the International Conference on Database Theory.

Practical Tutorial

Hyper-heuristic Algorithms for Grouping Problems

Grouping problems are combinatorial problems that emerge in several numerous real-world applications. One of the most outstanding metaheuristics to solve NP-hard grouping problems is the Grouping Genetic Algorithm (GGA). This tutorial presents an experimental approach to designing high-level GGAs by incorporating intelligent rules or learning methods to automate the process of selecting, combining, generating, or adapting several simpler heuristics (or components of such heuristics) to solve computational search problems efficiently. We will start with a general introduction to a case study, the R||Cmax grouping problem, as well as an introduction to Grouping Genetic Algorithms. In the second part, we will focus on the classification of Hyper-heuristic Algorithms. For this purpose, we will present different approaches to recognize their operation and contextualize the procedure of a Hyper-heuristic Grouping Genetic Algorithm with an Online Selection of Variation Operators for the R||Cmax grouping problem, which will be coded in Google Colab. We will close the tutorial by discussing possible future research paths in this direction.

Bio

Dr. Quiroz received her PhD in Computer Science from the Instituto Tecnológico de Tijuana and her M.Sc. in Computer Science from the Instituto Tecnológico de Ciudad Madero. She is a member of the National System of Researchers (SNI), the Mexican Society of Computer Science, and the boards of the Mexican Academy of Computing and the Mexican Federation of Robotics.

She is a founding member of the outreach group Código IA. She is currently the coordinator of the PhD in Artificial Intelligence and a researcher at the Artificial Intelligence Research Institute of the Universidad Veracruzana, where she develops research in the Computational Learning and Intelligent Optimization domain. In particular, in the context of experimental algorithms, metaheuristics, genetic algorithms, bin packing, machine learning, causal inference applications, logistics, and distributed systems.

Practical Tutorial

Principles of Blockchain

The tutorial will cover the basic concepts of the Blockchain as well as its types, elements, advantages and disadvantages. At the end of this tutorial, the attendee will be able to implement a small application that will allow him/her to decide the best applications of this technology technology.

Bio

Rocío Aldeco-Pérez, Ph.D. in Computer Science from the University of Southampton in the United Kingdom, is a researcher specializing in Cryptography and Security. She currently serves as a full-time associate professor in the Department of Computer Science at the School of Engineering at UNAM, where she also holds the position of Department Head.

Dr. Aldeco-Pérez is passionate about research and teaching. Her research interests encompass critical areas such as information privacy, decentralized and distributed cryptographic protocols, as well as the development of secure applications using blockchain technology. Throughout her career, she has led numerous research and consulting projects in these domains, supervised academic theses, and made significant contributions to scholarly publications.

In addition to her academic activities, Dr. Aldeco-Pérez is a member of the Mexican Academy of Computing and served on the executive committee of ACM-W North America. She is actively involved in initiatives like “Women in STEM, Future Leaders” and “Women in Computing,” which aim to promote the participation of girls and women in the fields of Science, Technology, Engineering, and Mathematics (STEM).