Virtual On line
June 22, 2020
June 22, 2020
June 26, 2021
Women in Engineering
Machine learning and artificial intelligence (ML/AI) technology can be biased through non-representative training and testing activities leading to discriminatory and negative social consequences. The enormous potential of ML/AI to shape the future of technology underscores the need to increase the diversity of workers within the field, with one group of untapped talent being women engineers. An unresolved contradiction exists between the trend of greater woman representation in broader STEM fields and the consistently low numbers of women engineers pursuing careers in ML/AI. Furthermore, there has been a lack of tailored research investigating the potential causes of such under-representation. Professional role confidence has been shown to be a significant and positive predictor of persistence of women in STEM. However, this link has not yet been established specifically within the field of ML/AI. For this study, we surveyed targeted undergraduate students at a major international university. Students reported on their predictors of persistence including their professional role confidence in ML/AI, their experiences with discrimination, their career exposure, and their internal drivers. We present three models using Ordinal Logistic Regression to determine the effect of those predictors on Intentional Persistence. We found that higher levels of Expertise Confidence and Career Fit Confidence were related to higher levels of persistence in ML/AI careers. We also found that women who experienced discrimination from their instructors were less likely to persist in engineering and that discrimination was more prevalent for women than for men. Focusing on those predictors of intentional persistence, our study calls for efforts to correct the under-representation of women in ML/AI.
Ren, K., & Olechowski, A. (2020, June), Gendered Professional Role Confidence and Persistence of Artificial Intelligence and Machine Learning Students Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34704
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