10 research outputs found
Operationalizing Individual Fairness with Pairwise Fair Representations
We revisit the notion of individual fairness proposed by Dwork et al. A
central challenge in operationalizing their approach is the difficulty in
eliciting a human specification of a similarity metric. In this paper, we
propose an operationalization of individual fairness that does not rely on a
human specification of a distance metric. Instead, we propose novel approaches
to elicit and leverage side-information on equally deserving individuals to
counter subordination between social groups. We model this knowledge as a
fairness graph, and learn a unified Pairwise Fair Representation (PFR) of the
data that captures both data-driven similarity between individuals and the
pairwise side-information in fairness graph. We elicit fairness judgments from
a variety of sources, including human judgments for two real-world datasets on
recidivism prediction (COMPAS) and violent neighborhood prediction (Crime &
Communities). Our experiments show that the PFR model for operationalizing
individual fairness is practically viable.Comment: To be published in the proceedings of the VLDB Endowment, Vol. 13,
Issue.
Operationalizing Individual Fairness with Pairwise Fair Representations
We revisit the notion of individual fairness proposed by Dwork et al. A central challenge in operationalizing their approach is the difficulty in eliciting a human specification of a similarity metric. In this paper, we propose an operationalization of individual fairness that does not rely on a human specification of a distance metric. Instead, we propose novel approaches to elicit and leverage side-information on equally deserving individuals to counter subordination between social groups. We model this knowledge as a fairness graph, and learn a unified Pairwise Fair Representation(PFR) of the data that captures both data-driven similarity between individuals and the pairwise side-information in fairness graph. We elicit fairness judgments from a variety of sources, including humans judgments for two real-world datasets on recidivism prediction (COMPAS) and violent neighborhood prediction (Crime & Communities). Our experiments show that the PFR model for operationalizing individual fairness is practically viable
Fairness for Cooperative Multi-Agent Learning with Equivariant Policies
We study fairness through the lens of cooperative multi-agent learning. Our
work is motivated by empirical evidence that naive maximization of team reward
yields unfair outcomes for individual team members. To address fairness in
multi-agent contexts, we introduce team fairness, a group-based fairness
measure for multi-agent learning. We then incorporate team fairness into policy
optimization -- introducing Fairness through Equivariance (Fair-E), a novel
learning strategy that achieves provably fair reward distributions. We then
introduce Fairness through Equivariance Regularization (Fair-ER) as a
soft-constraint version of Fair-E and show that Fair-ER reaches higher levels
of utility than Fair-E and fairer outcomes than policies with no equivariance.
Finally, we investigate the fairness-utility trade-off in multi-agent settings.Comment: 15 pages, 4 figure
Learning Certified Individually Fair Representations
Fair representation learning provides an effective way of enforcing fairness
constraints without compromising utility for downstream users. A desirable
family of such fairness constraints, each requiring similar treatment for
similar individuals, is known as individual fairness. In this work, we
introduce the first method that enables data consumers to obtain certificates
of individual fairness for existing and new data points. The key idea is to map
similar individuals to close latent representations and leverage this latent
proximity to certify individual fairness. That is, our method enables the data
producer to learn and certify a representation where for a data point all
similar individuals are at -distance at most , thus
allowing data consumers to certify individual fairness by proving
-robustness of their classifier. Our experimental evaluation on five
real-world datasets and several fairness constraints demonstrates the
expressivity and scalability of our approach.Comment: Conference Paper at NeurIPS 202