错误的轨道：大数据和受保护的类别
Wrong side of the tracks: Big Data and Protected Categories
摘要：
当我们将机器学习用于公共政策时，我们发现许多有用的变量都与其他变量相关联，而这些变量作为决策的基础在伦理上是有问题的。这个问题在大数据时代变得尤为严重，因为预测往往是在缺乏强有力的因果机制理论的情况下做出的。我们描述了民主决策的危险时，高性能的算法无法提供一个明确的因果关系。然后，我们演示了如何信息论允许我们降低预测，使他们从受保护的变量去相关的准确性损失最小。然而，强制执行完全去相关充其量只是一个短期解决方案。因果论证在伦理辩论中的作用促使人们开发新的、可解释的机器学习算法，这些算法参考了因果机制。
英文摘要：
When we use machine learning for public policy, we find that many useful variables are associated with others on which it would be ethically problematic to base decisions. This problem becomes particularly acute in the Big Data era, when predictions are often made in the absence of strong theories for underlying causal mechanisms. We describe the dangers to democratic decision-making when high-performance algorithms fail to provide an explicit account of causation. We then demonstrate how information theory allows us to degrade predictions so that they decorrelate from protected variables with minimal loss of accuracy. Enforcing total decorrelation is at best a near-term solution, however. The role of causal argument in ethical debate urges the development of new, interpretable machine-learning algorithms that reference causal mechanisms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
