34,357 research outputs found
Metric Learning for Individual Fairness
There has been much discussion concerning how "fairness" should be measured or enforced in classification. Individual Fairness [Dwork et al., 2012], which requires that similar individuals be treated similarly, is a highly appealing definition as it gives strong treatment guarantees for individuals. Unfortunately, the need for a task-specific similarity metric has prevented its use in practice. In this work, we propose a solution to the problem of approximating a metric for Individual Fairness based on human judgments. Our model assumes access to a human fairness arbiter who is free of explicit biases and possesses sufficient domain knowledge to evaluate similarity. Our contributions include definitions for metric approximation relevant for Individual Fairness, constructions for approximations from a limited number of realistic queries to the arbiter on a sample of individuals, and learning procedures to construct hypotheses for metric approximations which generalize to unseen samples under certain assumptions of learnability of distance threshold functions
Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness
Adversarial perturbation is used to expose vulnerabilities in machine
learning models, while the concept of individual fairness aims to ensure
equitable treatment regardless of sensitive attributes. Despite their initial
differences, both concepts rely on metrics to generate similar input data
instances. These metrics should be designed to align with the data's
characteristics, especially when it is derived from causal structure and should
reflect counterfactuals proximity. Previous attempts to define such metrics
often lack general assumptions about data or structural causal models. In this
research, we introduce a causal fair metric formulated based on causal
structures that encompass sensitive attributes. For robustness analysis, the
concept of protected causal perturbation is presented. Additionally, we delve
into metric learning, proposing a method for metric estimation and deployment
in real-world problems. The introduced metric has applications in the fields
adversarial training, fair learning, algorithmic recourse, and causal
reinforcement learning
Learning fair representations
Abstract We propose a learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to the proportion in the population as a whole), and individual fairness (similar individuals should be treated similarly). We formulate fairness as an optimization problem of finding a good representation of the data with two competing goals: to encode the data as well as possible, while simultaneously obfuscating any information about membership in the protected group. We show positive results of our algorithm relative to other known techniques, on three datasets. Moreover, we demonstrate several advantages to our approach. First, our intermediate representation can be used for other classification tasks (i.e., transfer learning is possible); secondly, we take a step toward learning a distance metric which can find important dimensions of the data for classification
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints
Metric clustering is fundamental in areas ranging from Combinatorial
Optimization and Data Mining, to Machine Learning and Operations Research.
However, in a variety of situations we may have additional requirements or
knowledge, distinct from the underlying metric, regarding which pairs of points
should be clustered together. To capture and analyze such scenarios, we
introduce a novel family of \emph{stochastic pairwise constraints}, which we
incorporate into several essential clustering objectives (radius/median/means).
Moreover, we demonstrate that these constraints can succinctly model an
intriguing collection of applications, including among others \emph{Individual
Fairness} in clustering and \emph{Must-link} constraints in semi-supervised
learning. Our main result consists of a general framework that yields
approximation algorithms with provable guarantees for important clustering
objectives, while at the same time producing solutions that respect the
stochastic pairwise constraints. Furthermore, for certain objectives we devise
improved results in the case of Must-link constraints, which are also the best
possible from a theoretical perspective. Finally, we present experimental
evidence that validates the effectiveness of our algorithms.Comment: This paper appeared in AAAI 202
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.
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