Georgia Tech Researchers Improve Fairness in the Machine Learning Pipeline

Georgia Tech researchers have developed a new algorithm to mitigate bias from one of the first steps in the machine learning (ML) process. Known as fair principal component analysis (PCA), the new algorithm runs as fast as existing PCAs, but can reduce bias in low-dimensional representations of large datasets. Bias is one of the most pressing issues as ML is used for everything from image classification to determining loans. Although there are plenty of stories about obvious bias like ML algorithms only showing images of white men when asked to query the term "CEO," much of the bias is more insidious. Many researchers believe unfair ML is the result of biased data or faulty algorithms, but Tech researchers determined it can start as early as the data processing step.