Towards Collective Intelligence in Heterogeneous Learning
Abstract: Democratization of AI involves training and deploying machine learning models across heterogeneous and potentially massive environments. While a diversity of data can create new possibilities to advance AI systems, it simultaneously poses pressing concerns such as privacy, security, and equity that restricts the extent to which information can be shared across environments. Inspired by the social choice theory, I will first present a choice-theoretic perspective of machine learning as a tool to analyze learning algorithms in heterogeneous environments. To understand the fundamental limits, I will then provide a minimum requirement in terms of intuitive and reasonable axioms under which an empirical risk minimization (ERM) is the only rational learning algorithm in heterogeneous environments. This impossibility result implies that Collective Intelligence (CI), the ability of algorithms to successfully learn across heterogeneous environments, cannot be achieved without sacrificing at least one of these essential properties. Lastly, I will discuss the implications of this result in critical areas of machine learning such as out-of-distribution generalization, federated learning, algorithmic fairness, and multi-modal learning.
Bio: Krikamol Muandet is a tenure-track faculty member at CISPA Helmholtz Center for Information Security, Saarbrücken, Germany. Before joining CISPA, he was a research group leader in the Empirical Inference Department at the Max Planck Institute for Intelligent Systems (MPI-IS), Tübingen, Germany. He was a lecturer in the Department of Mathematics at Mahidol University, Bangkok, Thailand. He received his Ph.D. in computer science from the University of Tübingen in 2015 working mainly with Prof. Bernhard Schölkopf. He received his master's degree in machine learning from University College London (UCL), the United Kingdom where he worked mostly with Prof. Yee Whye Teh at Gatsby Computational Neuroscience Unit. He served as a publication chair of AISTATS 2021 and as an area chair for ICLR 2023, AISTATS 2022, NeurIPS 2021, NeurIPS 2020, NeurIPS 2019, and ICML 2019, among others.
His research interests include kernel methods, kernel mean embedding of distributions, learning under distributional shifts, domain generalization, counterfactual inference, and how to regulate the deployment of machine learning models.
What bias? Whose bias? A multi-level perspective on data and Artificial Intelligence.
Abstract: AI technologies such as machine learning, deep learning and artificial neural networks are reshaping data processing and analysis. They have been heralded as solutions to complex problems and are increasingly being used in a variety of sectors including communication, healthcare, and transportation. In light of their powerful transformative force and profound impact, recent scandals have sparked ample debate in academic literature and media coverage. Some AI systems have been shown to discriminate and generally fail to deliver on the promises made.
What are the origins of such failures, and what are the principles and values that should guide the development and use of AI? This talk will address these issues by centering on the notion of bias. Drawing on insights from STS (science and technology studies) as well as on critical algorithm studies, it will discuss why unbiased AI is neither achievable nor desirable. Data is never neutral, and data processing is always deeply intertwined with human decision making. Dealing with bias is therefore not only a technological matter but also one of policy and society.
Bio: Anna Jobin is a researcher with a multidisciplinary background in sociology, economics, and information management. Currently, she serves as a Senior Researcher at the Humboldt Institute for Internet and Society (HIIG) in Berlin, as well as a lecturer at EPFL (Lausanne, Switzerland) and the MCI (Innsbruck, Austria).
In addition, she is an inaugural member of the Swiss Young Academy, an advisory member at the ZeMKI affiliated with the Lab "Platform Governance, Media, and Technology" (University of Bremen), and an associate member of the Sciences and Technologies Studies Laboratory (STSLab) of Lausanne University. Previous affiliations include EPFL, ETH, Cornell University and Tufts University. Her research projects are situated at the crossing of science, technology, and society, with a particular focus on interaction with algorithmic systems, (digital) ethics in research and citizen science, and ethical artificial intelligence.
As an internationally recognized expert on the intersection of digital technology and society, her research and expertise have been featured in popular and specialized media alike. She volunteers as a board member of the Swiss STS Association and as a member of the steering committee of the Swiss Internet Governance Forum.
Since October 2021, Anna Jobin is president of the Swiss Federal Media Commission, an extra-parliamentary commission tasked with advising the Swiss Federal Council on media policy.