University of Toronto (U of T) Engineering alumna Kimberly Ren led a study that quantified predictors of whether women will choose careers in machine learning (ML) and artificial intelligence (AI).
The study of 279 undergraduate and graduate students at U of T Engineering studying ML/AI (38% female, 61% male) measured how several variables positively or negatively affected their persistence in pursuing careers in ML/AI or general engineering.
The study found that expertise confidence and career-fit confidence were significant positive predictors for both women and men, but gender discrimination from peers or teaching staff was a significant negative predictor only for female students.
Said Ren, "If we don't see a change, then biased teaching, inputs, algorithms, applications and decisions will lead to further discriminatory and negative social consequences."
From University of Toronto Engineering News
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