TRIADS Speaker Series: Teaching Responsible AI

Although several responsible data science and AI courses are 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, 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 AI courses that I have been developing and teaching to technical students at New York University since 2019. I will also speak about ongoing work on teaching responsible AI to members of the public in a peer learning setting, and about training practitioners in a range of domains and roles using case studies. The educational and training materials I will discuss are available at https://r-ai.co/education.

The TRIADS Speaker Series is co-sponsored by the Digital Intelligence & Innovation Accelerator.

Julia Stoyanovich's talk is co-sponsored by the Center for Empirical Research in the Law.

Bio:

Dr. Julia Stoyanovich is Institute Associate Professor of Computer Science and Engineering, Associate Professor of Data Science, Director of the Center for Responsible AI, and member of the Visualization and Data Analytics Research Center at New York University. Julia’s goal is to make “Responsible AI” synonymous with “AI”. She works towards this goal by engaging in academic research, education and technology policy, and by speaking about the benefits and harms of AI to practitioners and members of the public. Julia’s research interests include AI ethics and legal compliance, and data management and AI systems. In addition to academic publications, she has written for the New York Times, the Wall Street Journal, the LA Times, The Hill, and Le Monde. Julia has been teaching courses on responsible data science and AI to students, practitioners and the general public. She is engaged in technology policy and regulation in the US and internationally. Julia received her 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 the NSF CAREER Award and a Senior Member of the ACM.

RSVP