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In April 2023, the Simons Institute hosted a workshop on Multigroup Fairness and the Validity of Statistical Judgment, the latest in a series of workshops and clusters we’ve organized on the theme of algorithmic fairness, as part of our Algorithms, Society, and the Law initiative. 

In this episode of Polylogues, Simons Institute Director Shafi Goldwasser sits down with workshop leader Omer Reingold (Stanford) to explore the key themes of the workshop.

As the prevalence of machine learning expands across diverse domains, the role of algorithms in influencing decisions that significantly impact our lives becomes increasingly important. Concerns regarding the fairness of algorithmic decisions have spurred the proposal and investigation of the framework of multigroup fairness, which provides a mathematical foundation for assessing fairness across numerous overlapping subpopulations.

In this talk in the Simons Institute’s recent workshop on Multigroup Fairness and the Validity of Statistical Judgment, Rachel Lin (University of Washington) elucidates the close relationships among several recently proposed notions of multigroup fairness, namely, multi-accuracy, multi-calibration, and outcome indistinguishability, and concepts of pseudorandomness from complexity theory and cryptography, specifically leakage simulation in cryptography, weak regularity in complexity theory, and graph regularity in graph theory. By exploring these connections, Lin demonstrates that ideas in either area can lead to improvement in the other. 

Dear friends,

Greetings from the Simons Institute, where our Summer 2023 research programs are ramping up. 

One of the programs this summer is Analysis and TCS: New Frontiers. Back in Fall 2013, Real Analysis in Computer Science was one of the first programs hosted by the Simons Institute, making the current program a beautiful way to mark 10 years of programs at the Institute. 

Eighty outstanding researchers, innovators and communicators from around the world have been elected as the newest Fellows of the Royal Society, the UK’s national academy of sciences and the oldest science academy in continuous existence. 

Greetings from the Simons Institute, where we are wrapping up our Spring 2023 research program on Meta-Complexity and an extended reunion of the Satisfiability program. It has been a semester full of scientific discoveries, chances to make new friends and reconnect with old ones.

In his recent Theoretically Speaking public lecture, U.S. International Mathematical Olympiad team coach Po-Shen Loh (Carnegie Mellon) spoke on educational adaptation to generative AI.

The Simons Institute’s Breakthroughs lecture series highlights major research advances in theoretical computer science, as they happen. This spring, Raghu Meka presented joint work with Zander Kelley on one of the most important open problems in additive combinatorics. 

The term “mechanism” or “causal mechanism” is used in two possibly conflicting ways in causal inference literature. Sometimes “causal mechanism” is used to refer to the chain of causal relations that is unleashed between some stipulated triggering event (let’s call it X) and some outcome of interest (let’s call it Y). When people use the term in this sense, they mean “a causal process through which the effect of a treatment on an outcome comes about.” One could think of this use of the term as slowing down a movie about the causal process between the moment when X is unleashed and when Y obtains so that we can see more distinct frames capturing ever-finer-grained descriptions of prior events triggering subsequent events as they unfold over time.

By imbuing enormous vectors with semantic meaning, we can get machines to reason more abstractly — and efficiently — than before. (Quanta Magazine)

The prestigious awards recognize scholars with impressive achievements who also show exceptional promise in fields ranging from the natural sciences to the creative arts.