Women continue to be underrepresented in leadership positions: a mere 9% of Fortune 500 CEOs are women. This is driven at least partly by gender stereotypes that associate men, but not women, with leadership-focused, agentic traits (e.g., being assertive and decisive). Given that these stereotypes are expressed in language, can we learn something about companies' attitudes towards women from how they speak? In this talk we will discuss how machine learning gives an insight into how women's association with leadership qualities varies across organizations, before focusing on what we can do about it. Specifically, we provide evidence that appointing women to the top tiers of management can help to mitigate deep-rooted stereotypes in a way that is enduring and has the potential to precipitate future hiring of women leaders.
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