Fair machine learning has become a significant research topic with broad societal impact. However, most fair learning methods require direct access to personal demographic data, which is increasingly restricted to use for protecting user privacy (e.g. by the EU General Data Protection Regulation). In this paper, we propose a distributed fair learning framework for protecting the privacy of demographic data. We assume this data is privately held by a third party, which can communicate with the data center (responsible for model development) without revealing the demographic information. We propose a principled approach to design fair learning methods under this framework, exemplify four methods and show they consistently outperform their existing counterparts in both fairness and accuracy across three real-world data sets. We theoretically analyze the framework, and prove it can learn models with high fairness or high accuracy, with their trade-offs balanced by a threshold variable.
Fairness without Demographics through Learning Graph of Gradients
Carnegie Mellon University（卡内基梅隆大学）；The Chinese University of Hong Kong（香港中文大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Tsinghua University（清华大学）
Heinz College of Information Systems and Public Policy, Carnegie Mellon University（卡内基梅隆大学海因茨信息系统与公共政策学院）
We propose a general approach for training survival analysis models that minimizes a worst-case error across all subpopulations that are large enough (occurring with at least a user-specified minimum probability). This approach uses a training loss function that does not know any demographic information to treat as sensitive. Despite this, we demonstrate that our proposed approach often scores better on recently established fairness metrics (without a significant drop in prediction accuracy) compared to various baselines, including ones which directly use sensitive demographic information in t…
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Google Research（谷歌研究院）
Much of the previous machine learning (ML) fairness literature assumes that protected features such as race and sex are present in the dataset, and relies upon them to mitigate fairness concerns. However, in practice factors like privacy and regulation often preclude the collection of protected features, or their use for training or inference, severely limiting the applicability of traditional fairness research. Therefore we ask: How can we train an ML model to improve fairness when we do not even know the protected group memberships? In this work we address this problem by proposing Adversari…
Grad2Fair: A Gradient-driven Approach for Graph Fairness without Demographics
School of Computer Science and Engineering, Sun Yat-Sen University（中山大学计算机科学与工程学院）；School of Software Engineering, Sun Yat-sen University（中山大学软件工程学院）；Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究所）
Observed events in recommendation are consequence of the decisions made by a policy, thus they are usually selectively labeled, namely the data are Missing Not At Random (MNAR), which often causes large bias to the estimate of true outcomes risk. A general approach to correct MNAR bias is performing small Randomized Controlled Trials (RCTs), where an additional uniform policy is employed to randomly assign items to each user. In this work, we concentrate on the fairness of RCTs under both homogeneous and heterogeneous demographics, especially analyzing the bias for the least favorable group on…
Towards Harmless Rawlsian Fairness Regardless of Demographic Prior
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Institute of High-Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）；Centre for Frontier AI Research, Agency for Science, Technology and Research（新加坡科技研究局前沿人工智能研究中心）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Making fair decisions is crucial to ethically implementing machine learning algorithms in social settings. In this work, we consider the celebrated definition of counterfactual fairness [Kusner et al., NeurIPS, 2017]. We begin by showing that an algorithm which satisfies counterfactual fairness also satisfies demographic parity, a far simpler fairness constraint. Similarly, we show that all algorithms satisfying demographic parity can be trivially modified to satisfy counterfactual fairness. Together, our results indicate that counterfactual fairness is basically equivalent to demographic pari…
Are demographically invariant models and representations in medical imaging fair?
Technical University of Denmark（丹麦技术大学）；Pioneer Centre for AI（人工智能先锋中心）；Research Institute for Signals, Systems and Computational Intelligence (CONICET)（信号、系统与计算智能研究所（CONICET））；Universidad Nacional del Litoral（国立 littoral 大学）；University of Copenhagen（哥本哈根大学）；Neurobiology Research Unit, Rigshospitalet（哥本哈根大学医院神经生物学研究单元）
Massachusetts Institute of Technology（麻省理工学院）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Toyota Technological Institute at Chicago（芝加哥丰田工业大学）
Sensitive attributes such as race are rarely available to learners in real world settings as their collection is often restricted by laws and regulations. We give a scheme that allows individuals to release their sensitive information privately while still allowing any downstream entity to learn non-discriminatory predictors. We show how to adapt non-discriminatory learners to work with privatized protected attributes giving theoretical guarantees on performance. Finally, we highlight how the methodology could apply to learning fair predictors in settings where protected attributes are only av…
Machine learning models (e.g., speech recognizers) are usually trained to minimize average loss, which results in representation disparity---minority groups (e.g., non-native speakers) contribute less to the training objective and thus tend to suffer higher loss. Worse, as model accuracy affects user retention, a minority group can shrink over time. In this paper, we first show that the status quo of empirical risk minimization (ERM) amplifies representation disparity over time, which can even make initially fair models unfair. To mitigate this, we develop an approach based on distributionally…
Classifiers that achieve demographic balance by explicitly using protected attributes such as race or gender are often politically or culturally controversial due to their lack of individual fairness, i.e. individuals with similar qualifications will receive different outcomes. Individually and group fair decision criteria can produce counter-intuitive results, e.g. that the optimal constrained boundary may reject intuitively better candidates due to demographic imbalance in similar candidates. Both approaches can be seen as introducing individual resentment, where some individuals would have …
In recent years, there have been significant efforts on mitigating unethical demographic biases in machine learning methods. However, very little is done for kernel methods. In this paper, we propose a new fair kernel regression method via fair feature embedding (FKR-F$^2$E) in kernel space. Motivated by prior works on feature selection in kernel space and feature processing for fair machine learning, we propose to learn fair feature embedding functions that minimize demographic discrepancy of feature distributions in kernel space. Compared to the state-of-the-art fair kernel regression method…
Research has shown that, machine learning models might inherit and propagate undesired social biases encoded in the data. To address this problem, fair training algorithms are developed. However, most algorithms assume we know demographic/sensitive data features such as gender and race. This assumption falls short in scenarios where collecting demographic information is not feasible due to privacy concerns, and data protection policies. A recent line of work develops fair training methods that can function without any demographic feature on the data, that are collectively referred as Rawlsian …
When dealing with sensitive data in automated data-driven decision-making, an important concern is to learn predictors with high performance towards a class label, whilst minimising for the discrimination towards any sensitive attribute, like gender or race, induced from biased data. A few hybrid tree optimisation criteria exist that combine classification performance and fairness. Although the threshold-free ROC-AUC is the standard for measuring traditional classification model performance, current fair tree classification methods mainly optimise for a fixed threshold on both the classificati…
Partnership on AI（人工智能伙伴关系）；Minnesota State University, Mankato（明尼苏达州立大学曼卡托分校）；Sony AI（索尼人工智能）
As calls for fair and unbiased algorithmic systems increase, so too does the number of individuals working on algorithmic fairness in industry. However, these practitioners often do not have access to the demographic data they feel they need to detect bias in practice. Even with the growing variety of toolkits and strategies for working towards algorithmic fairness, they almost invariably require access to demographic attributes or proxies. We investigated this dilemma through semi-structured interviews with 38 practitioners and professionals either working in or adjacent to algorithmic fairne…
Beyond Demographic Balance: Multi-Metric and Intersectional Evaluation of Fairness in MIMIC-IV Mortality Prediction
Virginia Tech（弗吉尼亚理工大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；University of Melbourne（墨尔本大学）；University of Toronto（多伦多大学）；University of Virginia（弗吉尼亚大学）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）
On Demographic Group Fairness Guarantees in Deep Learning
Harvard AI and Robotics Lab at Harvard University（哈佛大学人工智能与机器人实验室）；Embodied AI and Robotics (AIR) Lab at New York University（纽约大学具身人工智能与机器人实验室）；School of Computing and Informatics, University of Louisiana at Lafayette（路易斯安那州立大学拉法叶分校计算机与信息学院）
Amazon Web Services（亚马逊网络服务）；Istituto Italiano di Tecnologia（意大利理工学院）；University College London（伦敦大学学院）
Developing learning methods which do not discriminate subgroups in the population is a central goal of algorithmic fairness. One way to reach this goal is by modifying the data representation in order to meet certain fairness constraints. In this work we measure fairness according to demographic parity. This requires the probability of the possible model decisions to be independent of the sensitive information. We argue that the goal of imposing demographic parity can be substantially facilitated within a multitask learning setting. We leverage task similarities by encouraging a shared fair re…
Organizations that own data face increasing legal liability for its discriminatory use against protected demographic groups, extending to contractual transactions involving third parties access and use of the data. This is problematic, since the original data owner cannot ex-ante anticipate all its future uses by downstream users. This paper explores the upstream ability to preemptively remove the correlations between features and sensitive attributes by mapping features to a fair representation space. Our main result shows that the fairness measured by the demographic parity of the representa…
University College London（伦敦大学学院）；Duke University（杜克大学）；Apple Inc.（苹果公司）
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. In this work, we study minimax group fairness in federated learning scenarios where different participating entities may only have access to a subset of the population groups during the training phase. We formally analyze how our proposed group fairness objective differs from existing federated learning fairness criteria that impose similar performance across participants instead of demographic groups. We provide an optimization algorithm -- FedMinMax -- for so…
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…
FairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA
Harvard AI and Robotics Lab（哈佛人工智能与机器人实验室）；Harvard University（哈佛大学）；Harvard Ophthalmology AI lab（哈佛眼科人工智能实验室）；University of Central Florida（佛罗里达中央大学）；University of Louisiana at Lafayette（路易斯安那州立大学拉斐特分校）；New York University（纽约大学）；NYUAD Center for Artificial Intelligence and Robotics（纽约大学阿布扎克人工智能与机器人中心）
Vector Institute（向量研究所）；Carnegie Mellon University（卡内基梅隆大学）；University of Cambridge（剑桥大学）；Microsoft Research（微软研究院）
We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups in the classification setting. Two key components underpinning the design of our algorithm are balanced error rate and conditional alignment of representations. We show how these two components contribute to ensuring accuracy parity and equalized false-positive and false-negative rates across groups without impacting demographic parity. Furthermore, we also demonstrate both in theory and on two real-world experiments that the proposed al…
