New York University
The Unbearable Lightness of Teaching Responsible Data Science
Although an increasing number of ethical data science and AI courses is available, pedagogical approaches used in these courses rely exclusively on texts rather than on algorithmic development or data analysis. Technical students often consider these courses unimportant and a distraction from the “real” material. To develop instructional materials and methodologies that are thoughtful and engaging, it is important to strike a balance: between texts and coding, between critique and solution, and between cutting-edge research and practical applicability. Finding such balance is difficult in the nascent field of responsible data science (RDS), where we are only starting to understand how to interface between the intrinsically different methodologies of engineering and social sciences. In this talk, Julia Stoyanovich will discuss responsible data science courses that she has been developing and teaching to technical students at New York University since 2019. These courses tackle a breadth of issues, including ethics in AI, legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, and privacy and data protection. All course materials are publicly available at https://dataresponsibly.github.io/courses. Stoyanovich will also speak to some ongoing work on teaching responsible data science to members of the public in a peer learning setting, and will feature a comic book series on responsible data science that is targeted at current and future practitioners.
Julia Stoyanovich is an Assistant Professor in the Department of Computer Science and Engineering at the Tandon School of Engineering, and the Center for Data Science. She is a recipient of an NSF CAREER award and of an NSF/CRA CI Fellowship. 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, and serves on the New York City Automated Decision Systems Task Force (by appointment by Mayor de Blasio). 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.