Julia Stoyanovich, Institute Associate Professor of Computer Science & Engineering at NYU's Tandon School of Engineering, will present "Teaching Responsible Data Science."
Abstract: Although an increasing number of ethical data science and AI courses is available, pedagogical approaches used in these courses rely primarily 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, we must strive for balance: between texts and coding, between critique and solution, and between cutting-edge research and practical applicability. In this talk, I will discuss responsible data science courses that I have been developing and teaching to technical students at New York University since 2019. I will also speak about a public education course called "We are AI" that is offered in a peer-learning setting. I will draw on these efforts to chart a path towards strengthening the distributed accountability structures, to make the design, development, use, and oversight of automated decision systems responsible.
Julia Stoyanovich is an Associate 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.
- Falaah Arif Khan and Julia Stoyanovich. “Mirror, Mirror”. Data, Responsibly Comics, Volume 1 (2020). Retrieved from: https://dataresponsibly.github.io/comics/vol1/mirror_en.pdf
- Falaah Arif Khan, Eleni Manis, and Julia Stoyanovich. “Fairness and Friends”. Data, Responsibly Comics, Volume 2 (2021). https://dataresponsibly.github.io/comics/vol2/fairness_en.pdf
- Stoyanovich, J., Howe, B., & Jagadish, H. V. (2020). Responsible data management. In Proceedings of the VLDB Endowment (Vol. 13, Issue 12, pp. 3474–3488). Retrieved from: https://doi.org/10.14778/3415478.3415570
Meeting ID: 870 8734 0754
Questions? Contact Janet Eke
The Responsible Data Science and AI Speaker Series discusses topics such as explainability, reproducibility, biases, data curation and governance, and privacy. The series is co-organized by Associate Professor Jana Diesner and Assistant Professor Nigel Bosch at the iSchool at Illinois and hosted by the iSchool's Center for Informatics Research in Science and Scholarship (CIRSS).
This event is sponsored by Center for Informatics Research in Science and Scholarship