Fairness is a fundamental requirement for trustworthy and human-centered Artificial Intelligence (AI) system. However, deep neural networks (DNNs) tend to make unfair predictions when the training data are collected from different sub-populations with different attributes (i.e. color, sex, age), leading to biased DNN predictions. We notice that such a troubling phenomenon is often caused by data itself, which means that bias information is encoded to the DNN along with the useful information (i.e. class information, semantic information). Therefore, we propose to use sketching to handle this phenomenon. Without losing the utility of data, we explore the image-to-sketching methods that can maintain useful semantic information for the target classification while filtering out the useless bias information. In addition, we design a fair loss to further improve the model fairness. We evaluate our method through extensive experiments on both general scene dataset and medical scene dataset. Our results show that the desired image-to-sketching method improves model fairness and achieves satisfactory results among state-of-the-art.
Universidad Autonoma de Madrid（马德里自治大学）；Max Planck Institute for Human Development（马克斯·普朗克人类发展研究所）
We propose a discrimination-aware learning method to improve both accuracy and fairness of biased face recognition algorithms. The most popular face recognition benchmarks assume a distribution of subjects without paying much attention to their demographic attributes. In this work, we perform a comprehensive discrimination-aware experimentation of deep learning-based face recognition. We also propose a general formulation of algorithmic discrimination with application to face biometrics. The experiments include tree popular face recognition models and three public databases composed of 64,000 …
Massachusetts Institute of Technology（麻省理工学院）；Google Brain（谷歌大脑）
In the quest towards general artificial intelligence (AI), researchers have explored developing loss functions that act as intrinsic motivators in the absence of external rewards. This paper argues that such research has overlooked an important and useful intrinsic motivator: social interaction. We posit that making an AI agent aware of implicit social feedback from humans can allow for faster learning of more generalizable and useful representations, and could potentially impact AI safety. We collect social feedback in the form of facial expression reactions to samples from Sketch RNN, an LST…
S$^3$Attention: Improving Long Sequence Attention with Smoothed Skeleton Sketching
Alibaba Group（阿里巴巴集团）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；University of Central Florida（中佛罗里达大学）；Peking University（北京大学）；Antai College of Economics and Management, Shanghai Jiao Tong University（上海交通大学安泰经济与管理学院）；Meta
University at Buffalo, State University of New York（纽约州立大学布法罗分校）；Carnegie Mellon University（卡内基梅隆大学）；Indiana University–Purdue University Indianapolis（印第安纳大学与普渡大学印第安纳波利斯联合分校）
Massachusetts Institute of Technology（麻省理工学院）；University of Toronto（多伦多大学）；University of Tübingen（蒂宾根大学）；University of Adelaide（阿德莱德大学）；Stanford University（斯坦福大学）
Deep learning models have reached or surpassed human-level performance in the field of medical imaging, especially in disease diagnosis using chest x-rays. However, prior work has found that such classifiers can exhibit biases in the form of gaps in predictive performance across protected groups. In this paper, we question whether striving to achieve zero disparities in predictive performance (i.e. group fairness) is the appropriate fairness definition in the clinical setting, over minimax fairness, which focuses on maximizing the performance of the worst-case group. We benchmark the performan…
There has been increasing awareness of ethical issues in machine learning, and fairness has become an important research topic. Most fairness efforts in computer vision have been focused on human sensing applications and preventing discrimination by people's physical attributes such as race, skin color or age by increasing visual representation for particular demographic groups. We argue that ML fairness efforts should extend to object recognition as well. Buildings, artwork, food and clothing are examples of the objects that define human culture. Representing these objects fairly in machine l…
Princeton University（普林斯顿大学）；Shanghai Jiaotong University（上海交通大学）；University of Pennsylvania（宾夕法尼亚大学）；RIKEN AIP（理化学研究所先进智能项目中心）；University of Tokyo（东京大学）
Fairness and accountability are two essential pillars for trustworthy Artificial Intelligence (AI) in healthcare. However, the existing AI model may be biased in its decision marking. To tackle this issue, we propose an adversarial multi-task training strategy to simultaneously mitigate and detect bias in the deep learning-based medical image analysis system. Specifically, we propose to add a discrimination module against bias and a critical module that predicts unfairness within the base classification model. We further impose an orthogonality regularization to force the two modules to be ind…
Skin lesions can be an early indicator of a wide range of infectious and other diseases. The use of deep learning (DL) models to diagnose skin lesions has great potential in assisting clinicians with prescreening patients. However, these models often learn biases inherent in training data, which can lead to a performance gap in the diagnosis of people with light and/or dark skin tones. To the best of our knowledge, limited work has been done on identifying, let alone reducing, model bias in skin disease classification and segmentation. In this paper, we examine DL fairness and demonstrate the …
Sun Yat-Sen University（中山大学）；The Chinese University of Hong Kong（香港中文大学）
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 propo…
Improving Performance, Robustness, and Fairness of Radiographic AI Models with Finely-Controllable Synthetic Data
Center for Artificial Intelligence in Medicine and Imaging, Stanford University（斯坦福大学人工智能医学与成像中心）；Department of Radiology, Stanford University（斯坦福大学放射科）；Department of Applied Physics, Stanford University（斯坦福大学应用物理系）；Department of Biomedical Data Science, Stanford University（斯坦福大学生物医学数据科学系）；Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich（苏黎世大学医院诊断与介入放射学研究所）；LAION；Juelich Supercomputing Center (JSC), Research Center Juelich (FZJ)（耶鲁超级计算中心（JSC）、耶鲁研究中心（FZJ））；Department of Radiology & Imaging Sciences, Emory University（埃默里大学放射科与成像科学系）；Department of Radiology, University of Florida College of Medicine（佛罗里达大学医学院放射科）
