Shion Guha joined the Faculty of Information in 2021 to help develop its human-centered data science concentration. Guha’s new co-authored textbook, which will be published on March 1, sets out best practices for addressing the bias and inequality that may result from automated data collection, analysis, and distribution of large datasets about.
In this interview with the Schwartz Reisman Institute (SRI), where Guha is a faculty affiliate, he reflects on the challenges of using algorithmic decision-making in public policy, and how to make better data-driven systems by incorporating human-centered design.
SRI: Can you tell us about your background?
Human-Centered Data Science will be published on March 1
Shion Guha: My academic background is primarily in statistics and machine learning. I graduated with my PhD from Cornell in 2016, and then was an assistant professor at Marquette University for five years before joining the Faculty of Information last year. U of T is one of the first universities in the world to launch an academic program in human-centered data science, so I was nudged to apply. Read the full interview
You can also find out more about the work of Professor Guha and the Faculty’s other data scientists in our Research Report.
