Predictably Unequal? The Effects of Machine Learning on Credit Markets
研究使用美国抵押贷款数据，比较传统模型与机器学习模型预测违约的效果，发现黑人和西班牙裔借款人从机器学习中获益较少，且机器学习加剧了群体内外的利率差异。
ABSTRACT Innovations in statistical technology in functions including credit‐screening have raised concerns about distributional impacts across categories such as race. Theoretically, distributional effects of better statistical technology can come from greater flexibility to uncover structural relationships or from triangulation of otherwise excluded characteristics. Using data on U.S. mortgages, we predict default using traditional and machine learning models. We find that Black and Hispanic borrowers are disproportionately less likely to gain from the introduction of machine learning. In a simple equilibrium credit market model, machine learning increases disparity in rates between and within groups, with these changes attributable primarily to greater flexibility.