Mitigating biases in machine learning

Machine Learning (ML) is increasingly being used to simplify and automate a number of important computational tasks in modern society. From the disbursement of bank loans to job application screenings, these computer systems streamline several processes that have a considerable impact on our day to day lives. However, these artificially intelligent systems are most often devised to emulate human decision making -- an inherently biased framework. For example, Microsoft's Tay online chatbot quickly learned to tweet using racial slurs as a result of the biased online input stream (Caton and Haas 2020), and the COMPAS tool often flagged black individuals as more likely to commit a crime (even if two individuals were statistically similar with respect to many other attributes) (Flores, Bechtel and Lowenkamp 2016). Crucially, these issues are not the product of a malevolent computer programmer instilling radical beliefs, but rather a byproduct of machines learning to optimize for a particular objective, which can inadvertently leverage underlying biases present in the data.