21,550 research outputs found
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
Latent Space Smoothing for Individually Fair Representations
Fair representation learning encodes user data to ensure fairness and
utility, regardless of the downstream application. However, learning
individually fair representations, i.e., guaranteeing that similar individuals
are treated similarly, remains challenging in high-dimensional settings such as
computer vision. In this work, we introduce LASSI, the first representation
learning method for certifying individual fairness of high-dimensional data.
Our key insight is to leverage recent advances in generative modeling to
capture the set of similar individuals in the generative latent space. This
allows learning individually fair representations where similar individuals are
mapped close together, by using adversarial training to minimize the distance
between their representations. Finally, we employ randomized smoothing to
provably map similar individuals close together, in turn ensuring that local
robustness verification of the downstream application results in end-to-end
fairness certification. Our experimental evaluation on challenging real-world
image data demonstrates that our method increases certified individual fairness
by up to 60%, without significantly affecting task utility
Certifying and removing disparate impact
What does it mean for an algorithm to be biased? In U.S. law, unintentional
bias is encoded via disparate impact, which occurs when a selection process has
widely different outcomes for different groups, even as it appears to be
neutral. This legal determination hinges on a definition of a protected class
(ethnicity, gender, religious practice) and an explicit description of the
process.
When the process is implemented using computers, determining disparate impact
(and hence bias) is harder. It might not be possible to disclose the process.
In addition, even if the process is open, it might be hard to elucidate in a
legal setting how the algorithm makes its decisions. Instead of requiring
access to the algorithm, we propose making inferences based on the data the
algorithm uses.
We make four contributions to this problem. First, we link the legal notion
of disparate impact to a measure of classification accuracy that while known,
has received relatively little attention. Second, we propose a test for
disparate impact based on analyzing the information leakage of the protected
class from the other data attributes. Third, we describe methods by which data
might be made unbiased. Finally, we present empirical evidence supporting the
effectiveness of our test for disparate impact and our approach for both
masking bias and preserving relevant information in the data. Interestingly,
our approach resembles some actual selection practices that have recently
received legal scrutiny.Comment: Extended version of paper accepted at 2015 ACM SIGKDD Conference on
Knowledge Discovery and Data Minin
Direct certification of a class of quantum simulations
One of the main challenges in the field of quantum simulation and computation
is to identify ways to certify the correct functioning of a device when a
classical efficient simulation is not available. Important cases are situations
in which one cannot classically calculate local expectation values of state
preparations efficiently. In this work, we develop weak-membership formulations
of the certification of ground state preparations. We provide a non-interactive
protocol for certifying ground states of frustration-free Hamiltonians based on
simple energy measurements of local Hamiltonian terms. This certification
protocol can be applied to classically intractable analog quantum simulations:
For example, using Feynman-Kitaev Hamiltonians, one can encode universal
quantum computation in such ground states. Moreover, our certification protocol
is applicable to ground states encodings of IQP circuits demonstration of
quantum supremacy. These can be certified efficiently when the error is
polynomially bounded.Comment: 10 pages, corrected a small error in Eqs. (2) and (5
Individual Fairness in Bayesian Neural Networks
We study Individual Fairness (IF) for Bayesian neural networks (BNNs).
Specifically, we consider the --individual fairness notion,
which requires that, for any pair of input points that are -similar
according to a given similarity metrics, the output of the BNN is within a
given tolerance We leverage bounds on statistical sampling over the
input space and the relationship between adversarial robustness and individual
fairness to derive a framework for the systematic estimation of
--IF, designing Fair-FGSM and Fair-PGD as
global,fairness-aware extensions to gradient-based attacks for BNNs. We
empirically study IF of a variety of approximately inferred BNNs with different
architectures on fairness benchmarks, and compare against deterministic models
learnt using frequentist techniques. Interestingly, we find that BNNs trained
by means of approximate Bayesian inference consistently tend to be markedly
more individually fair than their deterministic counterparts
In Re: LifeUSA Holding, Inc.
USDC for the Eastern District of Pennsylvani
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