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1430BD ISR
426 Thompson Street, Ann Arbor, MI

TransFAT: Translating Fairness, Accountability, and Transparency into Data Science Practice

ABSTRACT

Data science technology promises to improve people’s lives, accelerate scientific discovery and innovation, and bring about positive societal change. Yet, if not used responsibly, this same technology can reinforce inequity, limit accountability, and infringe on the privacy of individuals. In my talk I will give an overview of the “Data, Responsibly” project that aims to operationalize ethics and legal compliance in data science systems. In particular, I will speak about my involvement in efforts to regulate the use of data science and AI in New York City, and about the imperative to establish a broad and inclusive educational agenda around responsible data science.

SPEAKER BIO

Julia Stoyanovich is an Assistant Professor at New York University in the Department of Computer Science and Engineering at the Tandon School of Engineering, and the Center for Data Science. Julia’s research focuses on responsible data management and analysis practices: on operationalizing fairness, diversity, transparency, and data protection in all stages of the data acquisition and processing lifecycle. She established the Data, Responsibly consortium (https://dataresponsibly.github.io/), and serves on the New York City Automated Decision Systems Task Force, by appointment from Mayor de Blasio. In Spring 2019, Julia developed and is teaching a course on Responsible Data Science at NYU (https://dataresponsibly.github.io/courses/spring19/). In addition to data ethics, Julia works on management and analysis of preference 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 and Statistics from the University of Massachusetts at Amherst. Julia’s work has been funded by the NSF, BSF and by industry. She is a recipient of an NSF CAREER award and of an NSF/CRA CI Fellowship.

This event is organized by the Michigan Institute for Data Science and the Institute for Social Research.