Actionable Recourse in Linear Classification

TL;DR

This paper introduces integer programming methods to ensure individuals can alter actionable inputs to change decisions made by linear classifiers, highlighting the importance of recourse in fair machine learning.

Contribution

It presents a novel approach to guarantee recourse in linear classification models without modifying the models themselves.

Findings

Recourse can be significantly impacted by standard model development practices.

The proposed tools help stakeholders evaluate and improve recourse in decision-making models.

Experiments on credit scoring demonstrate practical applicability.

Abstract

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.