Predictive algorithms are now commonly used to distribute society's resources and
sanctions. But these algorithms can entrench and exacerbate inequities. To guard against …

Western societies are marked by diverse and extensive biases and inequality that are
unavoidably embedded in the data used to train machine learning. Algorithms trained on …

We evaluate the impacts of adopting algorithmic risk assessments in sentencing. We find
that judges changed sentencing practices in response to the risk assessment, but that …

1. See, eg, Solon Bamcas & Andrew D. Selbst, Big Data's Disparate Impact, 104 CALIF. L.
REV. 671 (2016)(employment); Pauline T. Kim, Data-Driven Discrimination at Work, 58 WM …

Notes. This figure plots race-specific release rates for the 268 judges in our sample against
rates of predicted pretrial misconduct among the set of released defendants. Predicted …

There is great concern about algorithmic racial bias in the risk assessment instruments
(RAIs) used in the criminal legal system. When testing for algorithmic bias, most research …

Decision makers, such as doctors, judges, and managers, make consequential choices
based on predictions of unknown outcomes. Do these decision makers make systematic …

We develop an economic perspective on algorithmic fairness. Algorithmic bias and fairness
issues are appearing in an increasing variety of economic research literatures. Our …

We show that lenders face more uncertainty when assessing default risk of historically under-
served groups in US credit markets and that this information disparity is a quantitatively …

Algorithmic decision-making can lead to discrimination against legally protected groups, but
measuring such discrimination is often hampered by a fundamental selection challenge. We …