Fairness Constraints: Mechanisms for Fair Classification
This addresses fairness issues in automated decisions that can disproportionately affect groups based on sensitive attributes, representing an incremental improvement in fair classification methods.
The paper tackles the problem of algorithmic decision-making systems lacking fairness by introducing a flexible mechanism for designing fair classifiers, showing on real-world data that it allows fine-grained control over fairness with often small accuracy costs.
Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services. These systems rely on complex learning methods and vast amounts of data to optimize the service functionality, satisfaction of the end user and profitability. However, there is a growing concern that these automated decisions can lead, even in the absence of intent, to a lack of fairness, i.e., their outcomes can disproportionately hurt (or, benefit) particular groups of people sharing one or more sensitive attributes (e.g., race, sex). In this paper, we introduce a flexible mechanism to design fair classifiers by leveraging a novel intuitive measure of decision boundary (un)fairness. We instantiate this mechanism with two well-known classifiers, logistic regression and support vector machines, and show on real-world data that our mechanism allows for a fine-grained control on the degree of fairness, often at a small cost in terms of accuracy.