Generative Adversarial Networks (GANs) advance face synthesis through learning the underlying distribution of observed data. Despite the high-quality generated faces, some minority groups can be rarely generated from the trained models due to a biased image generation process. To study the issue, we first conduct an empirical study on a pre-trained face synthesis model. We observe that after training the GAN model not only carries the biases in the training data but also amplifies them to some degree in the image generation process. To further improve the fairness of image generation, we propose an interpretable baseline method to balance the output facial attributes without retraining. The proposed method shifts the interpretable semantic distribution in the latent space for a more balanced image generation while preserving the sample diversity. Besides producing more balanced data regarding a particular attribute (e.g., race, gender, etc.), our method is generalizable to handle more than one attribute at a time and synthesize samples of fine-grained subgroups. We further show the positive applicability of the balanced data sampled from GANs to quantify the biases in other face recognition systems, like commercial face attribute classifiers and face super-resolution algorithms.
On the Fairness, Diversity and Reliability of Text-to-Image Generative Models
University of Western Australia（西澳大学）；University of Melbourne（墨尔本大学）；University of British Columbia（不列颠哥伦比亚大学）；Australian National University（澳大利亚国立大学）
Deep Generative Networks (DGNs) are extensively employed in Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and their variants to approximate the data manifold and distribution. However, training samples are often distributed in a non-uniform fashion on the manifold, due to costs or convenience of collection. For example, the CelebA dataset contains a large fraction of smiling faces. These inconsistencies will be reproduced when sampling from the trained DGN, which is not always preferred, e.g., for fairness or data augmentation. In response, we develop MaGNET, a novel…
Generative adversarial networks (GANs) are one of the greatest advances in AI in recent years. With their ability to directly learn the probability distribution of data, and then sample synthetic realistic data. Many applications have emerged, using GANs to solve classical problems in machine learning, such as data augmentation, class unbalance problems, and fair representation learning. In this paper, we analyze and highlight fairness concerns of GANs model. In this regard, we show empirically that GANs models may inherently prefer certain groups during the training process and therefore they…
Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness
Technical University of Darmstadt（达姆施塔特工业大学）；Hessian Center for Artificial Intelligence (hessian.AI)（黑森人工智能中心（hessian.AI））；German Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；LAION；Huggingface（Hugging Face）
FairCoT: Enhancing Fairness in Text-to-Image Generation via Chain of Thought Reasoning with Multimodal Large Language Models
School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦女王学院电子工程与计算机科学学院）；Department of Knowledge Technologies, Jožef Stefan Institute（Jožef Stefan研究所知识技术系）
It is well known that deep generative models have a rich latent space, and that it is possible to smoothly manipulate their outputs by traversing this latent space. Recently, architectures have emerged that allow for more complex manipulations, such as making an image look as though it were from a different class, or painted in a certain style. These methods typically require large amounts of training in order to learn a single class of manipulations. We present Transflow Learning, a method for transforming a pre-trained generative model so that its outputs more closely resemble data that we p…
Are generative models fair? A study of racial bias in dermatological image generation
Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València（瓦伦西亚理工大学 以人为本技术大学研究所）；Technical University of Denmark（丹麦技术大学）
This work tackles the issue of fairness in the context of generative procedures, such as image super-resolution, which entail different definitions from the standard classification setting. Moreover, while traditional group fairness definitions are typically defined with respect to specified protected groups -- camouflaging the fact that these groupings are artificial and carry historical and political motivations -- we emphasize that there are no ground truth identities. For instance, should South and East Asians be viewed as a single group or separate groups? Should we consider one race as a…
FairDiffusion: Enhancing Equity in Latent Diffusion Models via Fair Bayesian Perturbation
Harvard Medical School（哈佛医学院）；New York University（纽约大学）；New York University Abu Dhabi（纽约大学阿布扎比分校）；Harvard University（哈佛大学）；Broad Institute of MIT and Harvard（麻省理工学院与哈佛大学博德研究所）
Operationalizing Fairness in Text-to-Image Models: A Survey of Bias, Fairness Audits and Mitigation Strategies
AImotion Bavaria, Technische Hochschule Ingolstadt（AImotion巴伐利亚、因戈尔施塔特技术大学）；School of Transformation and Sustainability, Catholic University of Eichstätt-Ingolstadt（转型与可持续性学院，埃施塔特-因戈尔施塔特天主教大学）；Chair of Economic and Social Ethics, University of Hohenheim（经济与社会伦理系，霍亨海姆大学）
Fast, Scale-Adaptive, and Uncertainty-Aware Downscaling of Earth System Model Fields with Generative Machine Learning
Technical University Munich（慕尼黑工业大学）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）；University of Exeter（埃克塞特大学）
McGill University（麦吉尔大学）；Mila（米拉研究所）；Université de Montréal（蒙特利尔大学）
Despite being widely used, face recognition models suffer from bias: the probability of a false positive (incorrect face match) strongly depends on sensitive attributes such as the ethnicity of the face. As a result, these models can disproportionately and negatively impact minority groups, particularly when used by law enforcement. The majority of bias reduction methods have several drawbacks: they use an end-to-end retraining approach, may not be feasible due to privacy issues, and often reduce accuracy. An alternative approach is post-processing methods that build fairer decision classifier…
Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining
Department of Electrical \& Computer Engineering, University of Minnesota, MN, USA；Center for Magnetic Resonance Research, University of Minnesota, MN, USA
Fair representation learning transforms user data into a representation that ensures fairness and utility regardless of the downstream application. However, learning individually fair representations, i.e., guaranteeing that similar individuals are treated similarly, remains challenging in high-dimensional settings such as computer vision. In this work, we introduce LASSI, the first representation learning method for certifying individual fairness of high-dimensional data. Our key insight is to leverage recent advances in generative modeling to capture the set of similar individuals in the gen…
Johns Hopkins University Applied Physics Laboratory（约翰斯·霍普金斯大学应用物理实验室）
While deep learning (DL) approaches are reaching human-level performance for many tasks, including for diagnostics AI, the focus is now on challenges possibly affecting DL deployment, including AI privacy, domain generalization, and fairness. This last challenge is addressed in this study. Here we look at a novel method for ensuring AI fairness with respect to protected or sensitive factors. This method uses domain adaptation via training set enhancement to tackle bias-causing training data imbalance. More specifically, it uses generative models that allow the generation of more synthetic trai…
Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling
Samsung Advanced Institute of Technology (SAIT)（三星先进技术研究院）；Korea Advanced Institute of Science & Technology (KAIST)（韩国科学技术院）；Pohang University of Science and Technology (POSTECH)（浦项科技大学）
Semantics Disentanglement and Composition for Universal Image Coding with Efficiently LLM Reasoning and Generative Diffusion
Shanghai Jiao Tong University（上海交通大学）；Ningbo Key Laboratory of Spatial Intelligence and Digital Derivative（宁波空间智能与数字衍生关键实验室）；Ningbo Institute of Digital Twin（宁波数字孪生研究院）；Eastern Institute of Technology（东部技术研究院）；University of Science and Technology of China（中国科学技术大学）；Yokohama National University（Yokohama国立大学）
