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Algorithms trained to predict mismeasured proxy variables can reproduce and scale up racial bias. This mechanism of algorithmic bias is distinct from others in the literature and harder to detect. We show this using examples from health care, but the forces we consider apply to a range of other important social sectors.

There are widespread concerns that the growing use of machine learning algorithms in important decisions may reproduce and reinforce existing discrimination against legally protected groups. Most of the attention to date on issues of “algorithmic bias” or “algorithmic fairness” has come from computer scientists and machine learning researchers. We argue that concerns about algorithmic fairness are at least as much about questions of how discrimination manifests itself in data, decision-making under uncertainty, and optimal regulation. To fully answer these questions, an economic framework is necessary--and as a result, economists have much to contribute.

Concerns about the dissemination of spurious results have led to calls for pre-analysis plans (PAPs) to avoid ex-post “p-hacking.” But often the conceptual hypotheses being tested do not imply the level of specificity required for a PAP. In this paper we suggest a framework for PAPs that capitalize on the availability of causal machine-learning (ML) techniques, in which researchers combine specific aspects of the analysis with ML for the flexible estimation of unspecific remainders. A “cheap-lunch” result shows that the inclusion of ML produces limited worst-case costs in power, while offering a substantial upside from systematic specification searches.

Concerns that algorithms may discriminate against certain groups have led to numerous efforts to ‘blind’ the algorithm to race. We argue that this intuitive perspective is misleading and may do harm. Our primary result is exceedingly simple, yet often overlooked. A preference for fairness should not change the choice of estimator. Equity preferences can change how the estimated prediction function is used (e.g., different threshold for different groups) but the function itself should not change. We show in an empirical example for college admissions that the inclusion of variables such as race can increase both equity and efficiency.