Gender-based cognitive bias and design thinking in the work of Finnish IT professionals

Abstract

Context Cognitive bias is a concern in artificial intelligence (AI) development. Research shows the prominence of cognitive bias within algorithms. We argue that cognitive bias is more than training data, but rather development team composition. Design Thinking (DT) is an approach used to reduce bias via multidisciplinary expertise. The article presents a study examining DT in addressing gender-based cognitive bias in the Finnish information technology industry. Objective The aim was to examine how the gender of IT professionals influences familiarity with and use of DT, coupled with awareness and addressing of cognitive bias in IT development processes. Method A mixed method questionnaire was used to collect data from N = 93 participants. Questions probed familiarity with DT, use of DT, and cognitive bias handling in participants’ organizations. Non-parametric tests were used to analyze quantitative data, due to abnormal distributions. Atlas.ti was used to code and analyze the qualitative data. Categorization determined whether participants recognized bias in their work, and the importance they attributed towards dealing with gender-based bias in IT. Results Women were more likely to view gender-based cognitive bias as relevant. Women were significantly more familiar with DT as a methodology (p = 0.028), men were significantly more likely to engage in user studies (p = 0.018). Older participants showed a tendency to emphasize the importance of open discussion more than other participant groups, with some analyses indicating a trend-level difference (p = 0.085). Qualitative responses indicated the importance of discussion in development teams to avoid or mitigate bias, suggesting the need for organizational psychological safety. Conclusion The paper provides novel contributions to the human dimension of bias in AI and IT in general. Results show that men and women IT professionals were aware of DT, yet men professionals were more likely to mitigate bias through collecting insight from end-users.