2,354 research outputs found
Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation
Deep neural networks are highly susceptible to learning biases in visual
data. While various methods have been proposed to mitigate such bias, the
majority require explicit knowledge of the biases present in the training data
in order to mitigate. We argue the relevance of exploring methods which are
completely ignorant of the presence of any bias, but are capable of identifying
and mitigating them. Furthermore, we propose using Bayesian neural networks
with an epistemic uncertainty-weighted loss function to dynamically identify
potential bias in individual training samples and to weight them during
training. We find a positive correlation between samples subject to bias and
higher epistemic uncertainties. Finally, we show the method has potential to
mitigate visual bias on a bias benchmark dataset and on a real-world face
detection problem, and we consider the merits and weaknesses of our approach.Comment: To be published in 2022 IEEE CVPR Workshop on Fair, Data Efficient
and Trusted Computer Visio
MixFairFace: Towards Ultimate Fairness via MixFair Adapter in Face Recognition
Although significant progress has been made in face recognition, demographic
bias still exists in face recognition systems. For instance, it usually happens
that the face recognition performance for a certain demographic group is lower
than the others. In this paper, we propose MixFairFace framework to improve the
fairness in face recognition models. First of all, we argue that the commonly
used attribute-based fairness metric is not appropriate for face recognition. A
face recognition system can only be considered fair while every person has a
close performance. Hence, we propose a new evaluation protocol to fairly
evaluate the fairness performance of different approaches. Different from
previous approaches that require sensitive attribute labels such as race and
gender for reducing the demographic bias, we aim at addressing the identity
bias in face representation, i.e., the performance inconsistency between
different identities, without the need for sensitive attribute labels. To this
end, we propose MixFair Adapter to determine and reduce the identity bias of
training samples. Our extensive experiments demonstrate that our MixFairFace
approach achieves state-of-the-art fairness performance on all benchmark
datasets.Comment: Accepted in AAAI-23; Code: https://github.com/fuenwang/MixFairFac
Gradient Attention Balance Network: Mitigating Face Recognition Racial Bias via Gradient Attention
Although face recognition has made impressive progress in recent years, we
ignore the racial bias of the recognition system when we pursue a high level of
accuracy. Previous work found that for different races, face recognition
networks focus on different facial regions, and the sensitive regions of
darker-skinned people are much smaller. Based on this discovery, we propose a
new de-bias method based on gradient attention, called Gradient Attention
Balance Network (GABN). Specifically, we use the gradient attention map (GAM)
of the face recognition network to track the sensitive facial regions and make
the GAMs of different races tend to be consistent through adversarial learning.
This method mitigates the bias by making the network focus on similar facial
regions. In addition, we also use masks to erase the Top-N sensitive facial
regions, forcing the network to allocate its attention to a larger facial
region. This method expands the sensitive region of darker-skinned people and
further reduces the gap between GAM of darker-skinned people and GAM of
Caucasians. Extensive experiments show that GABN successfully mitigates racial
bias in face recognition and learns more balanced performance for people of
different races.Comment: Accepted by CVPR 2023 worksho
Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization
Current face recognition systems achieve high progress on several benchmark
tests. Despite this progress, recent works showed that these systems are
strongly biased against demographic sub-groups. Consequently, an easily
integrable solution is needed to reduce the discriminatory effect of these
biased systems. Previous work mainly focused on learning less biased face
representations, which comes at the cost of a strongly degraded overall
recognition performance. In this work, we propose a novel unsupervised fair
score normalization approach that is specifically designed to reduce the effect
of bias in face recognition and subsequently lead to a significant overall
performance boost. Our hypothesis is built on the notation of individual
fairness by designing a normalization approach that leads to treating similar
individuals similarly. Experiments were conducted on three publicly available
datasets captured under controlled and in-the-wild circumstances. Results
demonstrate that our solution reduces demographic biases, e.g. by up to 82.7%
in the case when gender is considered. Moreover, it mitigates the bias more
consistently than existing works. In contrast to previous works, our fair
normalization approach enhances the overall performance by up to 53.2% at false
match rate of 0.001 and up to 82.9% at a false match rate of 0.00001.
Additionally, it is easily integrable into existing recognition systems and not
limited to face biometrics.Comment: Accepted in Pattern Recognition Letter
Proposing a Roadmap for Designing Non-Discriminatory ML Services: Preliminary Results from a Design Science Research Project
Artificial Intelligence (AI) and Machine Learning (ML) algorithms are being developed with ever higher accuracy. However, the use of ML also has its dark side. In the recent past, examples have repeatedly emerged of ML systems learning discriminatory and even racist or sexist patterns and acting accordingly. As ML systems become an integral part of both private and economic spheres of life, academia and practice must address the question of how non-discriminatory ML algorithms can be developed to benefit everyone. This is where our research in progress paper contributes. Using a real-world smart living case study, we investigated discrimination in terms of ethnicity and gender within state-of-the-art pre-trained ML models for face recognition and quantified it using an F1 metric. Building on these empirical findings as well as on the state of the scientific literature, we propose a roadmap for further research on the development of non-discriminatory ML services
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