Please join us for a UW Data Science Seminar featuring UW Computer Science PhD student Teanna Barrett on Thursday, February 5th  from 4:30 to 5:20 p.m. PT. The seminar will be held in IEB G109.

“Moral Missions: Surfacing and Developing the Moral Decision-Making Processes of Responsible Data Scientists”

Abstract:

Data ethics and fairness research has generated a plethora of theories, techniques, and tools to help professional data scientists operationalize responsible data science. Yet, professional data scientists still report difficulty with operationalizing data ethics in their workflow. Through a review of my previous work on responsible data science intervention tools and African data ethics, I identify the missing piece to operationalizing data ethics: centering practitioners’ moral missions. I conceptualize moral missions as the journeys responsible data scientists take to pragmatically transform their personal values into technical actions. In sharing my theoretical grounding and initial interview study insights, I surface decision-making processes practitioners use to define, actualize, and refine their moral missions. I propose that centering the varied lived experiences of responsible data scientists will inform pragmatic and subsequently more effective responsible data science tools and systems.

Speaker Bio: Teanna Barrett (she/her) is a second year PhD student at the Paul G. Allen School of Computer Science & Engineering at the University of Washington. She earned her B.S. in Computer Science with a minor in Philosophy at Howard University. She is a 2025-2026 Herbold Fellow, 2024 – 2027 ARCS Fellow, and 2024-2025 College of Engineering Dean’s Fellow. She is co-advised by Amy Zhang and Leilani Battle. Her current research characterizes and design tools to develop data science praxis. In other words, she aims to understand how data scientists form social analyses to motivate their work and translate these motivations into technical practices (and vice versa). Towards this inquiry, Barrett engages with Black thought, data ethics, and human-centered design theories and techniques.