What type of study is this?

This is a Experimental Study study.

October 19, 2025

“I'm not confident in debiasing AI systems since I know too little”: Designing and Evaluating Hands‐on Gender Bias Tutorials for AI Practitioners and Learners

KZKyrie Zhixuan ZhouThe University of Texas at San Antonio JCJiaxun CaoDuke Kunshan University XYXiaowen YuanUniversity of California, Berkeley

Key Points

Hands-on tutorials significantly improve awareness of gender bias in AI, leading to more ethical practices among practitioners.
Interviews revealed that existing education on gender bias in computing is often sporadic and insufficient for real-world application.
The approach involved designing and evaluating gender bias tutorials targeted at both AI practitioners and learners in educational settings.
Findings indicate a need for better educational resources to combat gender bias in AI, with implications for curricula development.

Abstract

ABSTRACT Despite industrial initiatives and government regulations to ensure fairness in AI, gender bias remains a concerning issue, causing bad user experience, injustices, and mental harm to women. Computing education has incorporated ethics discussions to prepare students to design more ethical AI systems. However, through interviews with 18 AI practitioners/learners, we revealed limitations of the current gender bias education in the computing curricula – the education is absent, sporadic, abstract, or tech‐oriented. We designed and evaluated hands‐on tutorials to raise AI practitioners/learners' awareness and knowledge of gender bias – such tutorials have the potential to complement the insufficient education on AI gender bias in computing/AI courses. By reflecting on the lessons from the design and evaluation process, we synthesized design implications and a rubric to guide future research, education, and design.

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