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Model Mis-specification and Algorithmic Bias
We show that even with unbiased input data, when a model is mis-specified: (1) population-level mean prediction error can still be negligible, but group-level mean prediction errors can be large; (2) such errors are not equal across groups; and (3) the difference between errors, i. e., bias, can take the worst-case realization.
Crowd, Lending, Machine, and Bias
We then use the machine to make investment decisions, and find that the machine benefits not only the lenders but also the borrowers.
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