Actionable Recourse in Linear Classification

by   Berk Ustun, et al.

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 importantly -- will 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.


page 1

page 2

page 3

page 4


Bagging Supervised Autoencoder Classifier for Credit Scoring

Credit scoring models, which are among the most potent risk management t...

Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems

Machine learning based decision making systems are increasingly affectin...

Algorithmic and Economic Perspectives on Fairness

Algorithmic systems have been used to inform consequential decisions for...

Extracting Incentives from Black-Box Decisions

An algorithmic decision-maker incentivizes people to act in certain ways...

Calibration of Machine Learning Classifiers for Probability of Default Modelling

Binary classification is highly used in credit scoring in the estimation...

Debiasing Credit Scoring using Evolutionary Algorithms

This paper investigates the application of machine learning when trainin...

Prediction without Preclusion: Recourse Verification with Reachable Sets

Machine learning models are often used to decide who will receive a loan...

Please sign up or login with your details

Forgot password? Click here to reset