DeepMind is Using This Old Technique to Evaluate Fairness in Machine Learning Models

One of the arguments that is regularly used in favor of machine learning systems is the fact that they can arrive to decisions without being vulnerable to human subjectivity. However, that argument is only partially true. While machine learning systems don't make decisions based on feelings or emotions, they do inherit a lot of human biases via the training datasets. Bias is relevant because it leads to unfairness. In the last few years, there has been a lot of progress developing techniques that can mitigate the impact of bias and improve the fairness of machine learning systems.