  Equality of opportunity has emerged as an important ideal of distributive
justice. Empirically, Inequality of Opportunity (IOp) is measured in two steps:
first, an outcome (e.g., income) is predicted given individual circumstances;
and second, an inequality index (e.g., Gini) of the predictions is computed.
Machine Learning (ML) methods are tremendously useful in the first step.
However, they can cause sizable biases in IOp since the bias-variance trade-off
allows the bias to creep in the second step. We propose a simple debiased IOp
estimator robust to such ML biases and provide the first valid inferential
theory for IOp. We demonstrate improved performance in simulations and report
the first unbiased measures of income IOp in Europe. Mother's education and
father's occupation are the circumstances that explain the most. Plug-in
estimators are very sensitive to the ML algorithm, while debiased IOp
estimators are robust. These results are extended to a general U-statistics
setting.
