"I'm Not Confident in Debiasing AI Systems Since I Know Too Little": Teaching AI Creators About Gender Bias Through Hands-on Tutorials

• URL: http://arxiv.org/abs/2309.08121v1
• Date: Fri, 15 Sep 2023 03:09:36 GMT
• Title: "I'm Not Confident in Debiasing AI Systems Since I Know Too Little": Teaching AI Creators About Gender Bias Through Hands-on Tutorials
• Authors: Kyrie Zhixuan Zhou, Jiaxun Cao, Xiaowen Yuan, Daniel E. Weissglass, Zachary Kilhoffer, Madelyn Rose Sanfilippo, Xin Tong
• Abstract summary: School curricula fail to educate AI creators on this topic. Gender bias is rampant in AI systems, causing bad user experience, injustices, and mental harm to women.
• Score: 11.823789408603908
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gender bias is rampant in AI systems, causing bad user experience, injustices, and mental harm to women. School curricula fail to educate AI creators on this topic, leaving them unprepared to mitigate gender bias in AI. In this paper, we designed hands-on tutorials to raise AI creators' awareness of gender bias in AI and enhance their knowledge of sources of gender bias and debiasing techniques. The tutorials were evaluated with 18 AI creators, including AI researchers, AI industrial practitioners (i.e., developers and product managers), and students who had learned AI. Their improved awareness and knowledge demonstrated the effectiveness of our tutorials, which have the potential to complement the insufficient AI gender bias education in CS/AI courses. Based on the findings, we synthesize design implications and a rubric to guide future research, education, and design efforts.

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