Actionable Recourse in Machine Learning

Jul 17, 2020



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.



About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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