Machine learning fairness concerns about the biases towards certain protected or sensitive group of people when addressing the target tasks. This paper studies the debiasing problem in the context of image classification tasks. Our data analysis on facial attribute recognition demonstrates (1) the attribution of model bias from imbalanced training data distribution and (2) the potential of adversarial examples in balancing data distribution. We are thus motivated to employ adversarial example to augment the training data for visual debiasing. Specifically, to ensure the adversarial generalization as well as cross-task transferability, we propose to couple the operations of target task classifier training, bias task classifier training, and adversarial example generation. The generated adversarial examples supplement the target task training dataset via balancing the distribution over bias variables in an online fashion. Results on simulated and real-world debiasing experiments demonstrate the effectiveness of the proposed solution in simultaneously improving model accuracy and fairness. Preliminary experiment on few-shot learning further shows the potential of adversarial attack-based pseudo sample generation as alternative solution to make up for the training data lackage.
The Johns Hopkins University Applied Physics Laboratory（约翰斯·霍普金斯大学应用物理实验室）；Queens University（女王大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
We propose a novel method for enforcing AI fairness with respect to protected or sensitive factors. This method uses a dual strategy performing training and representation alteration (TARA) for the mitigation of prominent causes of AI bias by including: a) the use of representation learning alteration via adversarial independence to suppress the bias-inducing dependence of the data representation from protected factors; and b) training set alteration via intelligent augmentation to address bias-causing data imbalance, by using generative models that allow the fine control of sensitive factors …
Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tone
Artefact Research Center, Paris, France（阿切夫特研究中心，法国巴黎）；Institut de Mathématiques de Toulouse (UMR 5219), CNRS, Université de Toulouse, F-31062 Toulouse, France（图卢兹数学研究所（UMR 5219），CNRS，图卢兹大学，法国图卢兹）；L’Oréal Research（欧莱雅研究）；INRIA, ANITI, France（INRIA，ANITI，法国）
If our models are used in new or unexpected cases, do we know if they will make fair predictions? Previously, researchers developed ways to debias a model for a single problem domain. However, this is often not how models are trained and used in practice. For example, labels and demographics (sensitive attributes) are often hard to observe, resulting in auxiliary or synthetic data to be used for training, and proxies of the sensitive attribute to be used for evaluation of fairness. A model trained for one setting may be picked up and used in many others, particularly as is common with pre-trai…
Deep learning models often learn to make predictions that rely on sensitive social attributes like gender and race, which poses significant fairness risks, especially in societal applications, e.g., hiring, banking, and criminal justice. Existing work tackles this issue by minimizing information about social attributes in models for debiasing. However, the high correlation between target task and social attributes makes bias mitigation incompatible with target task accuracy. Recalling that model bias arises because the learning of features in regard to bias attributes (i.e., bias features) hel…
HoloFair: Unified T2I Fairness Evaluation and Fair-GRPO Debiasing
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；School of Software Technology, Zhejiang University, Ningbo, China（浙江大学宁波校区软件学院）；Ningbo Global Innovation Center, Zhejiang University, Ningbo, China（浙江大学宁波全球创新中心）；Collaborative Innovation Center of Novel Software Technology and Industrialization（新型软件技术与产业化协同创新中心）
Large pre-trained vision-language models (VLMs) reduce the time for developing predictive models for various vision-grounded language downstream tasks by providing rich, adaptable image and text representations. However, these models suffer from societal biases owing to the skewed distribution of various identity groups in the training data. These biases manifest as the skewed similarity between the representations for specific text concepts and images of people of different identity groups and, therefore, limit the usefulness of such models in real-world high-stakes applications. In this work…
Towards Fair Medical AI: Adversarial Debiasing of 3D CT Foundation Embeddings
Rice University（莱斯大学）；The Johns Hopkins University（约翰斯·霍普金斯大学）；McGovern Medical School, UTHealth Houston（德克萨斯大学健康科学中心休斯顿分校麦戈文医学院）；The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院西德尼·基梅尔综合癌症中心）；Google Research（谷歌研究院）；Department of Neurosurgery, The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院神经外科）
debiaSAE: Benchmarking and Mitigating Vision-Language Model Bias
Johns Hopkins University（约翰斯·霍普金斯大学）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Harvard Medical School（哈佛医学院）；Mass General Brigham（麻省总医院布里格姆医疗系统）；Boston Children’s Hospital（波士顿儿童医院）
As transformer architectures become increasingly prevalent in computer vision, it is critical to understand their fairness implications. We perform the first study of the fairness of transformers applied to computer vision and benchmark several bias mitigation approaches from prior work. We visualize the feature space of the transformer self-attention modules and discover that a significant portion of the bias is encoded in the query matrix. With this knowledge, we propose TADeT, a targeted alignment strategy for debiasing transformers that aims to discover and remove bias primarily from query…
FAIRT2V: Training-Free Debiasing for Text-to-Video Diffusion Models
School of Computer Science and Engineering, University of New South Wales, Australia.（新南威尔士大学计算机科学与工程学院，澳大利亚）；State Key Laboratory for Novel Software Technology, Nanjing University, China（南京大学新型软件技术国家重点实验室，中国）
LightFair: Towards an Efficient Alternative for Fair T2I Diffusion via Debiasing Pre-trained Text Encoders
State Key Laboratory of AI Safety, Institute of Computing Technology, CAS（人工智能安全国家重点实验室，计算技术研究所，中国科学院）；School of Computer Science and Tech., University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；Peng Cheng Laboratory（鹏城实验室）
