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What's Contributing to the Striking Gender Gap in the AI Field?

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University of Toronto Engineering alumna Kimberly Ren.

The talent gap isnt closing, says University of Toronto Engineering alumna Kimberly Ren, who led the first study to quantitatively establish predictors for women pursuing Machine Learning/Artificial Intelligence careers.

Credit: Kimberly Ren

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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