Identifying and Correcting Label Bias in Machine Learning Lyrn.AI

As machine learning (ML) becomes more effective and widespread it is becoming more prevalent in systems with real-life impact, from loan recommendations to job application decisions. With the growing usage comes the risk of bias – biased training data could lead to biased ML algorithms, which in turn could perpetuate discrimination and bias in society. In a new paper from Google, researchers propose a novel technique to train machine learning algorithms fairly even with a biased dataset. At the heart of the technique is the idea that a biased dataset can be perceived as an unbiased dataset which has gone through manipulation by a biased agent. Using this framework, the biased dataset is re-weighted to fit the (theoretical) unbiased dataset, and only then fed into a machine learning algorithm as training data.