17,998 research outputs found
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
Generalizability and Application of the Skin Reflectance Estimate Based on Dichromatic Separation (SREDS)
Face recognition (FR) systems have become widely used and readily available
in recent history. However, differential performance between certain
demographics has been identified within popular FR models. Skin tone
differences between demographics can be one of the factors contributing to the
differential performance observed in face recognition models. Skin tone metrics
provide an alternative to self-reported race labels when such labels are
lacking or completely not available e.g. large-scale face recognition datasets.
In this work, we provide a further analysis of the generalizability of the Skin
Reflectance Estimate based on Dichromatic Separation (SREDS) against other skin
tone metrics and provide a use case for substituting race labels for SREDS
scores in a privacy-preserving learning solution. Our findings suggest that
SREDS consistently creates a skin tone metric with lower variability within
each subject and SREDS values can be utilized as an alternative to the
self-reported race labels at minimal drop in performance. Finally, we provide a
publicly available and open-source implementation of SREDS to help the research
community. Available at https://github.com/JosephDrahos/SRED
Measuring Bias in AI Models with Application to Face Biometrics: An Statistical Approach
The new regulatory framework proposal on Artificial Intelligence (AI)
published by the European Commission establishes a new risk-based legal
approach. The proposal highlights the need to develop adequate risk assessments
for the different uses of AI. This risk assessment should address, among
others, the detection and mitigation of bias in AI. In this work we analyze
statistical approaches to measure biases in automatic decision-making systems.
We focus our experiments in face recognition technologies. We propose a novel
way to measure the biases in machine learning models using a statistical
approach based on the N-Sigma method. N-Sigma is a popular statistical approach
used to validate hypotheses in general science such as physics and social areas
and its application to machine learning is yet unexplored. In this work we
study how to apply this methodology to develop new risk assessment frameworks
based on bias analysis and we discuss the main advantages and drawbacks with
respect to other popular statistical tests.Comment: 8 page
Fair GANs through model rebalancing with synthetic data
Deep generative models require large amounts of training data. This often
poses a problem as the collection of datasets can be expensive and difficult,
in particular datasets that are representative of the appropriate underlying
distribution (e.g. demographic). This introduces biases in datasets which are
further propagated in the models. We present an approach to mitigate biases in
an existing generative adversarial network by rebalancing the model
distribution. We do so by generating balanced data from an existing unbalanced
deep generative model using latent space exploration and using this data to
train a balanced generative model. Further, we propose a bias mitigation loss
function that shows improvements in the fairness metric even when trained with
unbalanced datasets. We show results for the Stylegan2 models while training on
the FFHQ dataset for racial fairness and see that the proposed approach
improves on the fairness metric by almost 5 times, whilst maintaining image
quality. We further validate our approach by applying it to an imbalanced
Cifar-10 dataset. Lastly, we argue that the traditionally used image quality
metrics such as Frechet inception distance (FID) are unsuitable for bias
mitigation problems
Survey of Social Bias in Vision-Language Models
In recent years, the rapid advancement of machine learning (ML) models,
particularly transformer-based pre-trained models, has revolutionized Natural
Language Processing (NLP) and Computer Vision (CV) fields. However, researchers
have discovered that these models can inadvertently capture and reinforce
social biases present in their training datasets, leading to potential social
harms, such as uneven resource allocation and unfair representation of specific
social groups. Addressing these biases and ensuring fairness in artificial
intelligence (AI) systems has become a critical concern in the ML community.
The recent introduction of pre-trained vision-and-language (VL) models in the
emerging multimodal field demands attention to the potential social biases
present in these models as well. Although VL models are susceptible to social
bias, there is a limited understanding compared to the extensive discussions on
bias in NLP and CV. This survey aims to provide researchers with a high-level
insight into the similarities and differences of social bias studies in
pre-trained models across NLP, CV, and VL. By examining these perspectives, the
survey aims to offer valuable guidelines on how to approach and mitigate social
bias in both unimodal and multimodal settings. The findings and recommendations
presented here can benefit the ML community, fostering the development of
fairer and non-biased AI models in various applications and research endeavors
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