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Our economys financial sector is using machine learning (ML) more often to support lending decisions that affect our daily lives. While technologies such as these pose new risks, they also have the potential to make lending fairer. Current regulation limits lenders use of ML and aims to reduce discrimination by preventing the use of variables correlated with protected class membership, such as race, age, or neighborhood, in any aspect of the lending decision. This research explores an alternative approach that would use an applicants neighborhood to consciously reduce fairness concerns between LMI and non-LMI applicants. Since this approach is costly to lenders and borrowers, we propose concurrent use with more advanced ML models that soften some of these costs by improving model predictions of default. The combination of embracing ML and setting explicit fairness goals may help address current disparities in credit access and ensure that the gains from innovations in ML are more widely shared. To successfully achieve these goals, a broad conversation should continue with stakeholders such as lenders, regulators, researchers, policymakers, technologists, and consumers.

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Advancing Fairness in Lending Through Machine Learning - Federal Reserve Bank of Philadelphia