Law Commons^™

The Input Fallacy, Talia B. Gillis

Faculty Scholarship

Algorithmic credit pricing threatens to discriminate against protected groups. Traditionally, fair lending law has addressed such threats by scrutinizing inputs. But input scrutiny has become a fallacy in the world of algorithms.

Using a rich dataset of mortgages, I simulate algorithmic credit pricing and demonstrate that input scrutiny fails to address discrimination concerns and threatens to create an algorithmic myth of colorblindness. The ubiquity of correlations in big data combined with the flexibility and complexity of machine learning means that one cannot rule out the consideration of protected characteristics, such as race, even when one formally excludes them. Moreover, using …