619 research outputs found
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
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
Adversarially-Aware Robust Object Detector
Object detection, as a fundamental computer vision task, has achieved a
remarkable progress with the emergence of deep neural networks. Nevertheless,
few works explore the adversarial robustness of object detectors to resist
adversarial attacks for practical applications in various real-world scenarios.
Detectors have been greatly challenged by unnoticeable perturbation, with sharp
performance drop on clean images and extremely poor performance on adversarial
images. In this work, we empirically explore the model training for adversarial
robustness in object detection, which greatly attributes to the conflict
between learning clean images and adversarial images. To mitigate this issue,
we propose a Robust Detector (RobustDet) based on adversarially-aware
convolution to disentangle gradients for model learning on clean and
adversarial images. RobustDet also employs the Adversarial Image Discriminator
(AID) and Consistent Features with Reconstruction (CFR) to ensure a reliable
robustness. Extensive experiments on PASCAL VOC and MS-COCO demonstrate that
our model effectively disentangles gradients and significantly enhances the
detection robustness with maintaining the detection ability on clean images.Comment: ECCV2022 oral pape
- …