Fair Predictors under Distribution Shift
Fair Predictors under Distribution Shift
复制标题
DOI：
--
复制
发表时间：
2019-11
期刊：
影响因子：
--
通讯作者：
Harvineet Singh;Rina Singh;Vishwali Mhasawade;R. Chunara
中科院分区：
文献类型：
--
作者：
Harvineet Singh;Rina Singh;Vishwali Mhasawade;R. Chunara
最近关于公平机器学习的工作增加了在高社会影响领域部署所需的一系列算法保障措施。模型部署的一个基本问题是在数据分布发生变化的情况下保证稳定的性能。领域适应的大量工作解决了这个问题，尽管稳定性的概念仅限于预测性能。我们提供的条件下，一个稳定的模型，无论是在预测和公平性能可以训练。基于因果域自适应的问题设置，我们选择了一个子集的功能训练预测与公平性约束，使风险相对于一个看不见的目标数据分布最小化。该方法的优点是证明在合成数据集上和在测量政策转变和选择偏差的情况下在现实世界的数据集中诊断急性肾损伤的任务。
Recent work on fair machine learning adds to a growing set of algorithmic safeguards required for deployment in high societal impact areas. A fundamental concern with model deployment is to guarantee stable performance under changes in data distribution. Extensive work in domain adaptation addresses this concern, albeit with the notion of stability limited to that of predictive performance. We provide conditions under which a stable model both in terms of prediction and fairness performance can be trained. Building on the problem setup of causal domain adaptation, we select a subset of features for training predictors with fairness constraints such that risk with respect to an unseen target data distribution is minimized. Advantages of the approach are demonstrated on synthetic datasets and on the task of diagnosing acute kidney injury in a real-world dataset under an instance of measurement policy shift and selection bias.