ICML Workshop on Algorithmic Recourse
July 24, 2021
The increasing popularity of machine learning (ML) models in various high-stakes domains such as finance, criminal justice and healthcare has resulted in a call for greater transparency into the mechanisms behind their predictions. Since transparency in algorithmic decision-making systems has undoubtedly become an ethical and sometimes legal obligation, there has been a recent surge of interest towards the field of algorithmic recourse. Algorithmic recourse aims to design mechanisms that provide actionable feedback about how to change the outcomes of ML models. For example, in the case of a ML model rejecting a loan application, an explanation that provides recourse could be: “To have your loan approved, you would need to increase your income by $10,000 per year”.
From counterfactual reasoning to re-applying for a loan: How do we connect the dots?
In this discussion, a diverse set of panelists across academia and industry will discuss what is necessary to successfully deploy algorithmic recourse in real-world systems. Audience questions will be encouraged to make the discussion more interactive.