论文标题
机器学习机会不平等
Machine Learning Inference on Inequality of Opportunity
论文作者
论文摘要
机会平等已成为分配正义的重要理想。从经验上讲，机会不平等（IOP）以两个步骤进行衡量：首先，在个人情况下，预测结果（例如收入）；其次，计算了预测的不平等指数（例如Gini）。机器学习（ML）方法在第一步非常有用。但是，由于偏见变化的权衡使偏见在第二步中蔓延，因此它们可能会引起IOP的巨大偏见。我们提出了一个简单的debias IOP估计量，以鲁棒性，并为IOP提供了第一个有效的推论理论。我们证明了在模拟中的性能提高，并报告了欧洲第一个公正的收入IOP衡量标准。母亲的教育和父亲的职业是解释最大的情况。插件估计器对ML算法非常敏感，而DEBIAS IOP估计器则很强。这些结果扩展到了一般的U统计设置。
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.
