Actionable Recourse in Machine Learning

Machine learning models are often used to automate decisions that affect consumers: whether to approve a loan, a credit card application or provide insurance. In such tasks, consumers should have the ability to change the decision of the model. When a consumer 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 so long as the model is deployed, and – more importantly – lack control over a decision that affects their livelihood. In this talk, I will formally discuss these issues in terms of a notion called recourse -- i.e., the ability of a person to change the decision of a model by altering actionable input variables. I will describe how machine learning models may fail to provide recourse due to standard practices in model development. I will then describe integer programming tools to verify recourse in linear classification models. I will end with a brief discussion on how recourse can facilitate meaningful consumer protection in modern applications of machine learning. This is joint work with Alexander Spangher and Yang Liu.