Actionable Recourse in Linear Classification
It addresses the need for fairness and control in automated decision-making systems affecting people's livelihoods, such as credit scoring, by providing a method to guarantee actionable recourse.
The paper tackles the problem of enabling individuals to change automated decisions (e.g., loan denials) by altering actionable input variables, and presents integer programming tools for ensuring recourse in linear classification, showing that standard model development practices can significantly affect recourse.
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.