Causal Interventions for Fairness
Causal Interventions for Fairness
复制标题
公平的因果干预
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Ricardo Silva
中科院分区：
文献类型：
--
作者：
Matt J. Kusner;Chris Russell;Joshua R. Loftus;Ricardo Silva
算法公平性中的大多数方法都会约束机器学习方法，因此最终的预测满足几个直观的公平性概念之一。虽然这可能有助于私营公司遵守非歧视法律或避免负面宣传，但我们认为这往往太少，太迟了。在收集培训数据时，弱势群体中的个人已经因无法控制的因素而遭受歧视和失去机会。在目前的工作中，我们专注于干预措施，如新的公共政策，特别是如何最大限度地发挥其积极作用，同时提高整个系统的公平性。我们使用因果方法来模拟干预的效果，考虑到潜在的干扰-每个人的结果可能取决于谁接受干预。我们证明了这一点的一个例子，分配预算的教学资源使用的数据集在纽约市的学校。
Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness. While this may help private companies comply with non-discrimination laws or avoid negative publicity, we believe it is often too little, too late. By the time the training data is collected, individuals in disadvantaged groups have already suffered from discrimination and lost opportunities due to factors out of their control. In the present work we focus instead on interventions such as a new public policy, and in particular, how to maximize their positive effects while improving the fairness of the overall system. We use causal methods to model the effects of interventions, allowing for potential interference--each individual's outcome may depend on who else receives the intervention. We demonstrate this with an example of allocating a budget of teaching resources using a dataset of schools in New York City.