(New York University, USA)
Abstract: Incorporating ethics and legal compliance into data-driven algorithmic systems has been attracting significant attention from the computing research community, most notably under the umbrella of fair and interpretable machine learning. Yet, much of this work has been limited to the “last mile” of data analysis, disregarding both the data lifecycle, and the lifecycle of a system’s design, development, and use. In my talk, I will argue that the decisions we make during data collection and preparation profoundly impact the robustness, fairness and interpretability of the systems we build, and that our responsibility for the operation of these systems does not stop once they are deployed. I will discuss technical work, and will place this work into the broader context of policy, education, and public outreach.
Bio: Julia Stoyanovich is an Institute Associate Professor of Computer Science & Engineering at the Tandon School of Engineering, Associate Professor of Data Science at the Center for Data Science, and Director of the Center for Responsible AI at New York University (NYU). Her research focuses on responsible data management and analysis: on operationalizing fairness, diversity, transparency, and data protection in all stages of the data science lifecycle. She established the “Data, Responsibly” consortium and served on the New York City Automated Decision Systems Task Force, by mayoral appointment. Julia developed and has been teaching courses on Responsible Data Science at NYU, and is a co-creator of an award-winning comic book series on this topic. In addition to data ethics, Julia works on the management and analysis of preference and voting data, and on querying large evolving graphs. She holds M.S. and Ph.D. degrees in Computer Science from Columbia University, and a B.S. in Computer Science and in Mathematics & Statistics from the University of Massachusetts at Amherst. She is a recipient of an NSF CAREER award and a Senior Member of the ACM.