Predictive AI is increasingly used to guide decisions on agents. I show that even a bias-neutral predictive AI can … potentially amplify exogenous (human) bias in settings where the predictive AI represents a cost-adjusted precision gain to … belief updating, expected victims of bias become less likely to be saved by randomness under more precise predictions. An …

We derive general, yet simple, sharp bounds on the size of the omitted variable bias for a broad class of causal … bound on the bias depends only on the additional variation that the latent variables create both in the outcome and in the … (in explaining treatment and outcome variation) are sufficient to place overall bounds on the size of the bias. Finally …

productivity distortions. The theoretical conditions necessary to completely eliminate bias are extreme and unlikely to appear in … a decrease (if not a full elimination) of bias, as has been documented in several empirical settings. The model makes …

Most definitions of algorithmic bias and fairness encode decisionmaker interests, such as profits, rather than the … interests of disadvantaged groups (e.g., racial minorities): Bias is defined as a deviation from profit maximization. Future … slippages between statistical notions of bias and misclassification errors, economic notions of profit, and normative notions of …