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Actionable Recourse in Machine Learning
Berk Ustun

Fri Jul 17 10:30 AM -- 11:00 AM (PDT) @ None
Event URL: https://slideslive.com/38930604/invited-talk-3 »

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.

Author Information

Berk Ustun (Harvard University)

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