| dc.description.abstract | Background: AI systems increase in popularity and widely implemented in many areas. Media and literature have reported numerous incidents of discriminating AI systems. Literature has identified several causes and solutions to gender bias in AI, and many institutions have published ethics guidelines. However, previous research has not studied the perspectives and practices of practitioners in AI. Aim: This thesis explores what perspectives practitioners in AI in Norway have on gender bias in AI by investigating their understanding of technology; how gender bias enters AI systems; and what practices they have in place to detect and address gender bias in AI. Method: Qualitative multiple case studies were conducted. This study interviewed 13 practitioners in the AI field in Norway. Thematic analysis was used to analyze the interviews. Findings: Practitioners have implemented few practices, most do not use any ethics guidelines, and they delegate responsibilities to other entities. The informants could only identify a few of the entry points of gender bias mentioned by literature, such as biased data, human bias, and a lack of diverse perspectives. The informants with at least one marginalized identity had more knowledge and practices to address gender bias in AI. They were able to identify more systemic causes and higher-impact levers of intervention. Conclusion: AI practitioners have inherited assumptions and beliefs from predecessors in the AI field on how distancing oneself from one's work achieves neutral objectivity. These beliefs have a significant influence on practitioners' understanding of technology, and as a result, few ethics practices are in place. These assumptions conflate their grasp of what causes gender bias in AI into a technical problem because they underestimate the effects of power. The practitioners see biased data as the main cause, but data is never neutral because no dataset is equally fair for everyone. The practitioners' belief that there exists a form of fairness that will always be correct for everyone at all times without considering the context enables biases to enter AI systems. The AI field needs to examine what technical heritage and taken-for-granted beliefs negatively impact research and practices on gender bias in AI. This study recommends a paradigm shift in practitioners from imagined objectivity to a critical, intersectional perspective that empowers, includes, and creates justice for disadvantaged groups. Inclusion of marginalized perspectives is crucial, and hiring practices should change to increase diversity by training disadvantaged groups in AI. | eng |