Author: Genevieve Smith, Ishita Rustagi
Publisher: Stanford Social Innovation Review
Publication Year: 2021
Summary: The following article discusses the effect of gender bias in data science. A very interesting example is the credit lines and credit scores given out to men and women with very similar profiles. In one case, a husband and wife compared their Apple credit lines only to find out the man had a credit line of 20 times that of his wife. The authors note that throughout history women have been underrepresented, thus there is a bias in all types of historical data. Moving forward, they suggest that we actively use feminist data practices to help fill the gaps in the data caused by the historical underrepresentation.
