Censored and Fair Universal Representations using Generative Adversarial
Models
release_6xrsq3ukwrdplfgc4r66udmsmy
by
Peter Kairouz and Jiachun Liao and Chong Huang and Lalitha Sankar
2020
Abstract
We present a data-driven framework for learning censored and fair
universal representations (CFUR) that ensure statistical fairness guarantees
for all downstream learning tasks that may not be known a priori. Our
framework leverages recent advancements in adversarial learning to allow a data
holder to learn censored and fair representations that decouple a set of
sensitive attributes from the rest of the dataset. The resulting problem of
finding the optimal randomizing mechanism with specific fairness/censoring
guarantees is formulated as a constrained minimax game between an encoder and
an adversary where the constraint ensures a measure of usefulness (utility) of
the representation. We show that for appropriately chosen adversarial loss
functions, our framework enables defining demographic parity for fair
representations and also clarifies the optimal adversarial strategy against
strong information-theoretic adversaries. We evaluate the performance of our
proposed framework on multi-dimensional Gaussian mixture models and publicly
datasets including the UCI Census, GENKI, Human Activity Recognition (HAR), and
the UTKFace. Our experimental results show that multiple sensitive features can
be effectively censored while ensuring accuracy for several a priori
unknown downstream tasks. Finally, our results also make precise the tradeoff
between censoring and fidelity for the representation as well as the
fairness-utility tradeoffs for downstream tasks.
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