Machine learning models are often used to decide who will receive a loan, a
job interview, or a public benefit. Standard techniques to build these models
use features about people but overlook their actionability. In turn, models can
assign predictions that are fixed, meaning that consumers who are denied loans,
interviews, or benefits may be permanently locked out from access to credit,
employment, or assistance. In this work, we introduce a formal testing
procedure to flag models that assign fixed predictions that we call recourse
verification. We develop machinery to reliably determine if a given model can
provide recourse to its decision subjects from a set of user-specified
actionability constraints. We demonstrate how our tools can ensure recourse and
adversarial robustness in real-world datasets and use them to study the
infeasibility of recourse in real-world lending datasets. Our results highlight
how models can inadvertently assign fixed predictions that permanently bar
access, and we provide tools to design algorithms that account for
actionability when developing models.

机器学习模型在决定贷款、工作面试或公共福利分配时经常使用。本文介绍了一种被称为回溯验证的形式化测试过程，用于标记分配固定预测的模型，并提供工具来设计在开发模型时考虑行动性的算法。