This blog post argues for the necessity of more women data scientists to combat bias in data-driven policymaking. It highlights that the male-dominated data science field (women hold only 18% of jobs in the US, less in lower-income countries) increases the risk of biased data, citing examples from crash test dummies to hiring algorithms. Increasing women’s participation in STEM and data science is crucial for equitable policy design, closing gender pay gaps, and ensuring that diverse perspectives are represented in decision-making processes.
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