Machine learning models are increasingly used to automate decisions that
affect humans - deciding who should receive a loan, a job interview, or a
social service. In such applications, a person should have the ability to
change the decision of a model. When a person is denied a loan by a credit
score, for example, they should be able to alter its input variables in a way
that guarantees approval. Otherwise, they will be denied the loan as long as
the model is deployed. More importantly, they will lack the ability to
influence a decision that affects their livelihood.
In this paper, we frame these issues in terms of recourse, which we define as
the ability of a person to change the decision of a model by altering
actionable input variables (e.g., income vs. age or marital status). We present
integer programming tools to ensure recourse in linear classification problems
without interfering in model development. We demonstrate how our tools can
inform stakeholders through experiments on credit scoring problems. Our results
show that recourse can be significantly affected by standard practices in model
development, and motivate the need to evaluate recourse in practice.

本文介绍了整数规划工具，以确保在线性分类问题中具有追索权，其中一个人通过更改可操作的输入变量（例如收入与年龄或婚姻状况）来改变模型的决策，并演示了这些工具如何通过信用评分问题的实验向利益相关者提供信息，结果表明，标准的模型开发实践可以极大地影响追索权，并促进实际中对追索权的评估。