The Input Fallacy
This paper critiques traditional approaches to fair lending that restrict certain inputs such as the consideration of protected class information (race, gender, etc.) or that require identifying inputs that cause disparities. Based on a simulation of algorithmic lending using mortgage lending data, the author argues that focusing on inputs fails to address core discrimination concerns. She also proposes an alternative fair lending framework to address the needs of algorithmic lenders and to recognize the potential limitations of explaining complex models.
Talia Gillis, Minnesota Law Review
Forthcoming 2022