15 research outputs found
FairCanary: Rapid Continuous Explainable Fairness
Machine Learning (ML) models are being used in all facets of today's society
to make high stake decisions like bail granting or credit lending, with very
minimal regulations. Such systems are extremely vulnerable to both propagating
and amplifying social biases, and have therefore been subject to growing
research interest. One of the main issues with conventional fairness metrics is
their narrow definitions which hide the complete extent of the bias by focusing
primarily on positive and/or negative outcomes, whilst not paying attention to
the overall distributional shape. Moreover, these metrics are often
contradictory to each other, are severely restrained by the contextual and
legal landscape of the problem, have technical constraints like poor support
for continuous outputs, the requirement of class labels, and are not
explainable.
In this paper, we present Quantile Demographic Drift, which addresses the
shortcomings mentioned above. This metric can also be used to measure
intra-group privilege. It is easily interpretable via existing attribution
techniques, and also extends naturally to individual fairness via the principle
of like-for-like comparison. We make this new fairness score the basis of a new
system that is designed to detect bias in production ML models without the need
for labels. We call the system FairCanary because of its capability to detect
bias in a live deployed model and narrow down the alert to the responsible set
of features, like the proverbial canary in a coal mine
Robust Fairness under Covariate Shift
Making predictions that are fair with regard to protected group membership
(race, gender, age, etc.) has become an important requirement for
classification algorithms. Existing techniques derive a fair model from sampled
labeled data relying on the assumption that training and testing data are
identically and independently drawn (iid) from the same distribution. In
practice, distribution shift can and does occur between training and testing
datasets as the characteristics of individuals interacting with the machine
learning system change. We investigate fairness under covariate shift, a
relaxation of the iid assumption in which the inputs or covariates change while
the conditional label distribution remains the same. We seek fair decisions
under these assumptions on target data with unknown labels. We propose an
approach that obtains the predictor that is robust to the worst-case in terms
of target performance while satisfying target fairness requirements and
matching statistical properties of the source data. We demonstrate the benefits
of our approach on benchmark prediction tasks