There has been long evidence and cases of discrimination in access to capital and lending opportunities in financial services. With the introduction of machine learning major concerns have arisen in regards to perpetuating historical human bias and in explaining credit decisions to consumers.

In this paper, we aim to define discrimination and interpretability in the context of fair lending and machine learning, and then to discuss important considerations and opportunities for machine learning in the equitable and transparent use of algorithms to underwrite credit lending decisions.

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