Delayed Impact of Fair Machine Learning

Machine learning systems trained to minimize prediction error may often exhibit discriminatory behavior based on sensitive characteristics such as race and gender. One reason could be due to historical bias in the data. In various application domains including lending, hiring, criminal justice, and advertising, machine learning has been criticized for its potential to harm historically underrepresented or disadvantaged groups. In this post, we talk about our recent work on aligning decisions made by machine learning with long term social welfare goals. Commonly, machine learning models produce a score that summarizes information about an individual in order to make decisions about them.