Econ Papers Daily / 期刊论文
偏见算法的利润
Profits of Prejudiced Algorithms
摘要
Firms are starting to replace humans with algorithms in important screening decisions, but there are potential spillovers of human biases contained in datasets to subsequent algorithmic predictions. When these biases are motivated by human prejudices, there are risks of algorithms perpetuating discrimination. I prove that when datasets are generated by a sufficiently discriminatory human, firms are more profitable when training discriminatory algorithms. If instead enough affirmative action is instituted in favor of a disadvantaged group, firms are more profitable when training algorithms that inflate scores for this group, but this effect diminishes with excess affirmative action.
中文摘要
企业开始在重要的筛选决策中用算法取代人类，但数据集中蕴含的人类偏见可能会溢出至后续的算法预测中。当这些偏见源于人类歧视时，算法存在延续歧视的风险。我证明，当数据集由具有足够歧视性的人类生成时，企业训练歧视性算法更为有利可图。相反，若对弱势群体实施充分的平权措施，企业训练为该群体抬高评分的算法将更有利可图，但这一效应会随着过度平权而减弱。