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Orthogonalizing Inputs, Talia B. Gillis

Faculty Scholarship

This paper examines an approach to algorithmic discrimination that seeks to blind predictions to protected characteristics by orthogonalizing inputs. The approach uses protected characteristics (such as race or sex) during the training phase of a model but masks these during deployment. The approach posits that including these characteristics in training prevents correlated features from acting as proxies, while assigning uniform values to them at deployment ensures decisions do not vary by group status.

Using a prediction exercise of loan defaults basedon mortgage HMDA data and German credit data, the paper highlights the limitations of this orthogonalization strategy. Applying a lasso …