Gender Biases in AI: Building Inclusive Future in Engineering Education

This paper explores gender bias in artificial intelligence (AI) and how AI systems may preserve or intensify existing societal gender biases. A literature review is conducted, in order to identify sources of bias, and two research questions were defined: a research question addressing impacts of gender bias in AI; and the role of engineering education in mitigating such bias during AI development. From the findings four themes emerged: Gender Bias, Inclusiveness, and AI Policy; Workplace Assessment, Decision-Making, and Social Impacts of AI Bias; Ethical Considerations and Strategies for AI Bias Mitigation; and, Algorithmic Fairness and Technical Debiasing Approaches. The study showed that existing literature regarding the engineering students training for gender bias mitigation in AI is in early consolidation stages. Based on literature review the authors present some proposals to mitigate gender bias in engineering education.