This event explored the critical intersection of algorithmic technologies, human speech and meaning-making.

Allison Koenecke is an Assistant Professor of Information Science at Cornell University. Her research interests lie broadly at the intersection of economics and computer science, focusing on algorithmic fairness. Her projects apply computational methods, such as machine learning and causal inference, to study societal inequities in domains from online services to public health. Allison is regularly quoted as an expert on racial disparities in automated speech-to-text systems. Previously, she was a postdoc at Microsoft Research New England in the Machine Learning and Statistics group. Before that, she received my PhD from Stanford’s Institute for Computational & Mathematical Engineering, where she worked in the Stanford Computational Policy Lab and the Golub Capital Social Impact Lab under the guidance of Susan Athey, Sharad Goel, and Hal Varian. Awards won include the NSF Graduate Research Fellowship and Forbes 30 Under 30 in Science.

Sayash Kapoor is a Ph.D. candidate at Princeton University’s Center for Information Technology Policy. His research critically investigates Machine Learning methods and their use in science and has been featured in WIRED, LA Times, and Nature among other media outlets. At Princeton University, he organized a workshop titled The Reproducibility Crisis in ML-based Science, which saw more than 1,700 registrants. He has worked on Machine Learning in several institutions in the industry and academia, including Facebook, Columbia University, and EPFL Switzerland. Sayash received a Best Paper award at ACM FAccT and an Impact Recognition award at ACM CSCW.

The event was moderated by Mona Sloane and supported by NYU’s Institute for Public Knowledge, the 370 Jay Project, the NYU Center for Responsible AI, and the NYU Tandon Department of Technology, Culture and Society.