Counterfactual Fairness
This addresses fairness issues in automated decision-making for affected populations, offering a novel causal approach that is incremental in applying existing tools to fairness.
The paper tackles the problem of biased machine learning decisions in areas like law school admissions by proposing a framework for counterfactual fairness, which ensures decisions remain unchanged in hypothetical scenarios where demographic attributes differ, and demonstrates it on a real-world law school prediction task.
Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a particular race, gender, or sexual orientation. Since this past data may be biased, machine learning predictors must account for this to avoid perpetuating or creating discriminatory practices. In this paper, we develop a framework for modeling fairness using tools from causal inference. Our definition of counterfactual fairness captures the intuition that a decision is fair towards an individual if it is the same in (a) the actual world and (b) a counterfactual world where the individual belonged to a different demographic group. We demonstrate our framework on a real-world problem of fair prediction of success in law school.