ABSTRACT. The increasingly pervasive use of big data and machine learning is raising various ethical issues, in particular privacy and fairness.In this talk, I will discuss some frameworks to understand and mitigate the issues, focusing on iterative methods coming from information theory and statistics.In the area of privacy protection, differential privacy (DP) and its variants are the most successful approaches to date. One of the fundamental issues of DP is how to reconcile the loss of information that it implies with the need to pr eserve the utility of the data. In this regard, a useful tool to recover utility is the Iterative Bayesian Update (IBU), an instance of the famous Expectation-Maximization method from Statistics. I will show that the IBU, combined with the metric version of DP, outperforms the state-of-the art, which is based on algebraic methods combined with the Randomized Response mechanism, widely adopted by the Big Tech industry (Google, Apple, Amazon, ...). Furthermore I will discuss a surprising duality between the IBU and one of the methods used to enhance metric DP, that is the Blahut-Arimoto algorithm from Rate-Distortion Theory. Finally, I will discuss the issue of biased decisions in machine learning, and will show that the IBU can be applied also in this domain to ensure a fairer treatment of disadvantaged groups.
Brief Bio:Catuscia Palamidessi is Director of Research at INRIA Saclay (since 2002), where she leads the team COMETE. She has been Full Professor at the University of Genova, Italy (1994-1997) and Penn State University, USA (1998-2002). Palamidessi's research interests include Privacy, Machine Learning, Fairness, Secure Information Flow, Formal Methods, and Concurrency. In 2019 she has obtained an ERC advanced grant to conduct research on Privacy and Machine Learning. She has been PC chair of various conferences including LICS and ICALP, and PC member of more than 120 international conferences. She is in the Editorial board of several journals, including the IEEE Transactions in Dependable and Secure Computing, Mathematical Structures in Computer Science, Theoretics, the Journal of Logical and Algebraic Methods in Programming and Acta Informatica. She is serving in the Executive Committee of ACM SIGLOG, CONCUR, and CSL.
ABSTRACT. Current AI systems lack several important human capabilities, such as adaptability, generalizability, self-control, consistency, common sense, and causal reasoning. We believe that existing cognitive theories of human decision making, such as the thinking fast and slow theory, can provide insights on how to advance AI systems towards some of these capabilities. In this talk, I will present the work done by IBM and collaborators in this space, including the definition of a general architecture that is based on fast/slow solvers and a metacognitive component. I will then present experimental results on the behavior of an instance of this architecture, for AI systems that make decisions about navigating in a constrained environment. The results will show how combining the fast and slow decision modalities allows the system to evolve over time and gradually pass from slow to fast thinking with enough experience, and that this greatly helps in decision quality, resource consumption, and efficiency.
Bio:
Francesca Rossi is an IBM Fellow and the IBM AI Ethics Global Leader. She is a computer scientist with over 30 years of experience in AI research. Before joining IBM, she has been a professor of computer science at the University of Padova, Italy, for 20 years. Her research interests focus on artificial intelligence, specifically they include constraint reasoning, preferences, multi-agent systems, computational social choice, and collective decision making. She is also interested in ethical issues in the development and behavior of AI systems, in particular for decision support systems for group decision making. She is a fellow of both AAAI and of EurAI and she has been president of IJCAI and the Editor in Chief of the Journal of AI Research. She will be the next president of AAAI. She co-leads the IBM AI ethics board and she actively participate in many global multi-stakeholder initiatives on AI ethics. She is a member of the board of directors of the Partnership on AI and the industry representative in the steering committee of the Global Partnership on AI. She is a fellow of both the worldwide association of AI (AAAI) and of the European one (EurAI), and she will be the next president of AAAI from July 2022.
