Here's how we can get more African women in machine learning and AI

Organizations are increasingly reliant on Machine Learning (ML) models to weigh in on decisions to hire, grant loans, sentence criminals, and release prisoners on parole. While it may seem that limiting the role of humans in such decisions would limit subjective biases, these ML models learn from data that are, in many cases, representative of existing societal biases. Researchers from Boston University and Microsoft have shown that software trained with text collected from Google News reproduced gender biases. When asked to complete the statement "Man is to computer programmer as woman is to [blank]," the trained software responded with "homemaker." Female representation is important in the fields of ML and AI to highlight, interrogate, and correct biases such as the ones implicit in the previous example.