173 research outputs found
Censoring Representations with an Adversary
In practice, there are often explicit constraints on what representations or
decisions are acceptable in an application of machine learning. For example it
may be a legal requirement that a decision must not favour a particular group.
Alternatively it can be that that representation of data must not have
identifying information. We address these two related issues by learning
flexible representations that minimize the capability of an adversarial critic.
This adversary is trying to predict the relevant sensitive variable from the
representation, and so minimizing the performance of the adversary ensures
there is little or no information in the representation about the sensitive
variable. We demonstrate this adversarial approach on two problems: making
decisions free from discrimination and removing private information from
images. We formulate the adversarial model as a minimax problem, and optimize
that minimax objective using a stochastic gradient alternate min-max optimizer.
We demonstrate the ability to provide discriminant free representations for
standard test problems, and compare with previous state of the art methods for
fairness, showing statistically significant improvement across most cases. The
flexibility of this method is shown via a novel problem: removing annotations
from images, from unaligned training examples of annotated and unannotated
images, and with no a priori knowledge of the form of annotation provided to
the model.Comment: Paper accepted to ICL
Fader Networks: Manipulating Images by Sliding Attributes
This paper introduces a new encoder-decoder architecture that is trained to
reconstruct images by disentangling the salient information of the image and
the values of attributes directly in the latent space. As a result, after
training, our model can generate different realistic versions of an input image
by varying the attribute values. By using continuous attribute values, we can
choose how much a specific attribute is perceivable in the generated image.
This property could allow for applications where users can modify an image
using sliding knobs, like faders on a mixing console, to change the facial
expression of a portrait, or to update the color of some objects. Compared to
the state-of-the-art which mostly relies on training adversarial networks in
pixel space by altering attribute values at train time, our approach results in
much simpler training schemes and nicely scales to multiple attributes. We
present evidence that our model can significantly change the perceived value of
the attributes while preserving the naturalness of images.Comment: NIPS 201
Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk
Guidelines for the management of atherosclerotic cardiovascular disease
(ASCVD) recommend the use of risk stratification models to identify patients
most likely to benefit from cholesterol-lowering and other therapies. These
models have differential performance across race and gender groups with
inconsistent behavior across studies, potentially resulting in an inequitable
distribution of beneficial therapy. In this work, we leverage adversarial
learning and a large observational cohort extracted from electronic health
records (EHRs) to develop a "fair" ASCVD risk prediction model with reduced
variability in error rates across groups. We empirically demonstrate that our
approach is capable of aligning the distribution of risk predictions
conditioned on the outcome across several groups simultaneously for models
built from high-dimensional EHR data. We also discuss the relevance of these
results in the context of the empirical trade-off between fairness and model
performance
FNNC: Achieving Fairness through Neural Networks
In classification models fairness can be ensured by solving a constrained
optimization problem. We focus on fairness constraints like Disparate Impact,
Demographic Parity, and Equalized Odds, which are non-decomposable and
non-convex. Researchers define convex surrogates of the constraints and then
apply convex optimization frameworks to obtain fair classifiers. Surrogates
serve only as an upper bound to the actual constraints, and convexifying
fairness constraints might be challenging.
We propose a neural network-based framework, \emph{FNNC}, to achieve fairness
while maintaining high accuracy in classification. The above fairness
constraints are included in the loss using Lagrangian multipliers. We prove
bounds on generalization errors for the constrained losses which asymptotically
go to zero. The network is optimized using two-step mini-batch stochastic
gradient descent. Our experiments show that FNNC performs as good as the state
of the art, if not better. The experimental evidence supplements our
theoretical guarantees. In summary, we have an automated solution to achieve
fairness in classification, which is easily extendable to many fairness
constraints
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