Recent studies found that using machine learning for social applications can lead to injustice in the form of racist, sexist, and otherwise unfair and discriminatory outcomes. To address this challenge, recent machine learning algorithms have been designed to limit the likelihood such unfair behavior occurs. However, these approaches typically assume the data used for training is representative of what will be encountered in deployment, which is often untrue. In particular, if certain subgroups of the population become more or less probable in deployment (a phenomenon we call demographic shift), prior work's fairness assurances are often invalid. In this paper, we consider the impact of demographic shift and present a class of algorithms, called Shifty algorithms, that provide high-confidence behavioral guarantees that hold under demographic shift when data from the deployment environment is unavailable during training. Shifty, the first technique of its kind, demonstrates an effective strategy for designing algorithms to overcome demographic shift's challenges. We evaluate Shifty using the UCI Adult Census dataset, as well as a real-world dataset of university entrance exams and subsequent student success. We show that the learned models avoid bias under demographic shift, unlike existing methods. Our experiments demonstrate that our algorithm's high-confidence fairness guarantees are valid in practice and that our algorithm is an effective tool for training models that are fair when demographic shift occurs.
@inproceedings{Giguere22iclr,
author = {Stephen Giguere and Blossom Metevier and Yuriy Brun and Bruno Castro {da Silva} and Philip S. Thomas and Scott Niekum},
title =
{\href{http://people.cs.umass.edu/brun/pubs/pubs/Giguere22iclr.pdf}{Fairness Guarantees under Demographic Shift}},
booktitle = {Proceedings of the 10th International Conference on Learning Representations (ICLR)},
venue = {ICLR},
accept = {$\frac{1,095}{3,391} \approx 32\%$},
month = {April},
date = {25--29},
year = {2022},
url = {https://openreview.net/forum?id=wbPObLm6ueA},
abstract = {<p>Recent studies found that using machine learning for social applications can
lead to injustice in the form of racist, sexist, and otherwise unfair and
discriminatory outcomes. To address this challenge, recent machine learning
algorithms have been designed to limit the likelihood such unfair behavior
occurs. However, these approaches typically assume the data used for training
is representative of what will be encountered in deployment, which is often
untrue. In particular, if certain subgroups of the population become more or
less probable in deployment (a phenomenon we call demographic shift), prior
work's fairness assurances are often invalid. In this paper, we consider the
impact of demographic shift and present a class of algorithms, called Shifty
algorithms, that provide high-confidence behavioral guarantees that hold
under demographic shift when data from the deployment environment is
unavailable during training. Shifty, the first technique of its kind,
demonstrates an effective strategy for designing algorithms to overcome
demographic shift's challenges. We evaluate Shifty using the UCI Adult Census
dataset, as well as a real-world dataset of university entrance exams and
subsequent student success. We show that the learned models avoid bias under
demographic shift, unlike existing methods. Our experiments demonstrate that
our algorithm's high-confidence fairness guarantees are valid in practice and
that our algorithm is an effective tool for training models that are fair
when demographic shift occurs.</p>},
fundedBy = {NSF CCF-1763423, NSF CCF-2018372, DEVCOM Army Research
Laboratory under Cooperative Agreement W911NF-17-2-0196 (ARL IoBT CRA),
Adobe, Google Research, Kosa.ai, and Meta Research},
}