Classification models are often used to make decisions that affect humans: whether to approve a loan application, extend a job offer, or provide insurance. In such applications, individuals should have the ability to change the decision of the model. When a person is denied a loan by a credit scoring model, for example, they should be able to change the input variables of the model in a way that will guarantee approval. Otherwise, this person will be denied the loan so long as the model is deployed, and -more importantlywill lack agency over a decision that affects their livelihood. In this paper, we propose to audit a linear classification model in terms of recourse, which we define as the ability of a person to change the decision of the model through actionable input variables (e.g., income vs. gender, age, or marital status). We present an integer programming toolkit to: (i) measure the feasibility and difficulty of recourse in a target population; and (ii) generate a list of actionable changes for an individual to obtain a desired outcome. We demonstrate how our tools can inform practitioners, policymakers, and consumers by auditing credit scoring models built using real-world datasets. Our results illustrate how recourse can be significantly impacted by common modeling practices, and motivate the need to guarantee recourse as a policy objective for regulation in algorithmic decision-making.
Motivated by goals similar to counterfactual explanations, REF propose an audit measure known as recourse, defined as the ability of a person to obtain a desired outcome from a fixed model.
Actionable Recourse in Linear Classification
ArXiv
ArXiv
Mathematics Computer Science