Towards Fair and Calibrated Models
Anand Brahmbhatt Vipul Rathore Mausam Parag Singla
Recent literature has seen a significant focus on building machine learning
models with specific properties such as fairness, i.e., being non-biased with
respect to a given set of attributes, calibration i.e., model confidence being
aligned with its predictive accuracy, and explainability, i.e., ability to be
understandable to humans. While there has been work focusing on each of these
aspects individually, researchers have shied away from simultaneously
addressing more than one of these dimensions. In this work, we address the
problem of building models which are both fair and calibrated. We work with a
specific definition of fairness, which closely matches [Biswas et. al. 2019],
and has the nice property that Bayes optimal classifier has the maximum
possible fairness under our definition. We show that an existing negative
result towards achieving a fair and calibrated model [Kleinberg et. al. 2017]
does not hold for our definition of fairness. Further, we show that ensuring
group-wise calibration with respect to the sensitive attributes automatically
results in a fair model under our definition. Using this result, we provide a
first cut approach for achieving fair and calibrated models, via a simple
post-processing technique based on temperature scaling. We then propose
modifications of existing calibration losses to perform group-wise calibration,
as a way of achieving fair and calibrated models in a variety of settings.
Finally, we perform extensive experimentation of these techniques on a diverse
benchmark of datasets, and present insights on the pareto-optimality of the
resulting solutions.