With the wide use of deep neural networks (DNN), model interpretability has become a critical concern, since explainable decisions are preferred in high-stake scenarios. Current interpretation techniques mainly focus on the feature attribution perspective, which are limited in indicating why and how particular explanations are related to the prediction. To this end, an intriguing class of explanations, named counterfactuals, has been developed to further explore the "what-if" circumstances for interpretation, and enables the reasoning capability on black-box models. However, generating counterfactuals for raw data instances (i.e., text and image) is still in the early stage due to its challenges on high data dimensionality and unsemantic raw features. In this paper, we design a framework to generate counterfactuals specifically for raw data instances with the proposed Attribute-Informed Perturbation (AIP). By utilizing generative models conditioned with different attributes, counterfactuals with desired labels can be obtained effectively and efficiently. Instead of directly modifying instances in the data space, we iteratively optimize the constructed attribute-informed latent space, where features are more robust and semantic. Experimental results on real-world texts and images demonstrate the effectiveness, sample quality as well as efficiency of our designed framework, and show the superiority over other alternatives. Besides, we also introduce some practical applications based on our framework, indicating its potential beyond the model interpretability aspect.
Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature Learning
School of Cyber Science and Engineering, Wuhan University（武汉大学网络空间安全学院）；School of Computing, Macquarie University（麦考瑞大学计算学院）；Renmin Hospital of Wuhan University（武汉大学人民医院）
University of Virginia（弗吉尼亚大学）；City University of Hong Kong（香港城市大学）；Microsoft（微软）
Fair machine learning aims to mitigate the biases of model predictions against certain subpopulations regarding sensitive attributes such as race and gender. Among the many existing fairness notions, counterfactual fairness measures the model fairness from a causal perspective by comparing the predictions of each individual from the original data and the counterfactuals. In counterfactuals, the sensitive attribute values of this individual had been modified. Recently, a few works extend counterfactual fairness to graph data, but most of them neglect the following facts that can lead to biases:…
Counterfactual explanation is an important Explainable AI technique to explain machine learning predictions. Despite being studied actively, existing optimization-based methods often assume that the underlying machine-learning model is differentiable and treat categorical attributes as continuous ones, which restricts their real-world applications when categorical attributes have many different values or the model is non-differentiable. To make counterfactual explanation suitable for real-world applications, we propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE), whic…
A machine learning model, under the influence of observed or unobserved confounders in the training data, can learn spurious correlations and fail to generalize when deployed. For image classifiers, augmenting a training dataset using counterfactual examples has been empirically shown to break spurious correlations. However, the counterfactual generation task itself becomes more difficult as the level of confounding increases. Existing methods for counterfactual generation under confounding consider a fixed set of interventions (e.g., texture, rotation) and are not flexible enough to capture d…
Automated computer vision systems have been applied in many domains including security, law enforcement, and personal devices, but recent reports suggest that these systems may produce biased results, discriminating against people in certain demographic groups. Diagnosing and understanding the underlying true causes of model biases, however, are challenging tasks because modern computer vision systems rely on complex black-box models whose behaviors are hard to decode. We propose to use an encoder-decoder network developed for image attribute manipulation to synthesize facial images varying in…
TabChange: Precise Attribute Changes in Tabular Data
The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；National Institute of Standards and Technology（美国国家标准与技术研究院）；Information Technology Laboratory（信息技术实验室）
Indian Institute of Technology Delhi（印度理工学院德里分校）；IBM Research - India（IBM研究院（印度））
Generating counterfactual test-cases is an important backbone for testing NLP models and making them as robust and reliable as traditional software. In generating the test-cases, a desired property is the ability to control the test-case generation in a flexible manner to test for a large variety of failure cases and to explain and repair them in a targeted manner. In this direction, significant progress has been made in the prior works by manually writing rules for generating controlled counterfactuals. However, this approach requires heavy manual supervision and lacks the flexibility to easi…
In recent years, fairness has become an important topic in the machine learning research community. In particular, counterfactual fairness aims at building prediction models which ensure fairness at the most individual level. Rather than globally considering equity over the entire population, the idea is to imagine what any individual would look like with a variation of a given attribute of interest, such as a different gender or race for instance. Existing approaches rely on Variational Auto-encoding of individuals, using Maximum Mean Discrepancy (MMD) penalization to limit the statistical de…
Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments
School of New Media and Communication, Tianjin University（天津大学新媒体与传播学院）；Department of Computer Science, Baylor University（贝勒大学计算机科学系）；GE Aerospace Research（GE航空航天研究院）；NEC Labs America（美国 NEC 实验室）
North Carolina State University（北卡罗来纳州立大学）；Indian Institute of Management（印度管理学院）
Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly. Recent work brings the discussion of machine learning fairness into the causal framework and elaborates on the concept of Counterfactual Fairness. In this paper, we develop the Fair Learning through dAta Preprocessing (FLAP) algorithm to learn counterfactually fair decisions from biased training data and formalize the conditions where different data preprocessing procedures should be used to guarantee counterfactual fairness. We also sho…
College of Computer Science and Technology, National University of Defense Technology, Changsha, China（计算机科学与技术学院，国防科技大学，长沙，中国）；Intelligent Game and Decision Lab, Academy of Military Science, Beijing, China（智能游戏与决策实验室，军事科学院，北京，中国）
We describe Countersynth, a conditional generative model of diffeomorphic deformations that induce label-driven, biologically plausible changes in volumetric brain images. The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations. Focusing on demographic attributes, we evaluate the quality of synthesized counterfactuals with voxel-based morphometry, clas…
Many existing methods of counterfactual explanations ignore the intrinsic relationships between data attributes and thus fail to generate realistic counterfactuals. Moreover, the existing models that account for relationships require domain knowledge, which limits their applicability in complex real-world applications. In this paper, we propose a novel approach to realistic counterfactual explanations that preserve the relationships and minimise experts' interventions. The model directly learns the relationships by a variational auto-encoder with minimal domain knowledge and then learns to per…
AI Alignment in Medical Imaging: Unveiling Hidden Biases Through Counterfactual Analysis
School of Computation, Information and Technology, Technical University of Munich（技术大学慕尼黑计算、信息与技术学院）；Department of Computer Science, University of Oxford（牛津大学计算机科学系）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Helmholtz AI, Munich, Germany（海德堡人工智能研究所，德国慕尼黑）
Institut de Mathématiques de Toulouse（图卢兹数学研究所）；University of the Basque Country（巴斯克大学）；IKERBASQUE（巴斯克科学基金会）；Institut Polytechnique de Paris（巴黎综合理工学院）；INRIA, Projet Regalia（法国国家信息与自动化研究所，Regalia项目）
Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF): A Data-Morphology-based Counterfactual Generation Method for Trustworthy Artificial Intelligence
Andalusian Institute of Data Science and Computational Intelligence (DASCI)（安达卢西亚数据科学与计算智能研究所）；University of Granada（格拉纳达大学）；TECNALIA（泰克纳利亚科技公司）；Basque Research & Technology Alliance (BRTA)（巴斯克研究与技术联盟）；University of the Basque Country (UPV/EHU)（巴斯克大学）
Counterfactual instances are a powerful tool to obtain valuable insights into automated decision processes, describing the necessary minimal changes in the input space to alter the prediction towards a desired target. Most previous approaches require a separate, computationally expensive optimization procedure per instance, making them impractical for both large amounts of data and high-dimensional data. Moreover, these methods are often restricted to certain subclasses of machine learning models (e.g. differentiable or tree-based models). In this work, we propose a deep reinforcement learning…
Implicit Counterfactual Data Augmentation for Robust Learning
National Engineering Research Center for Software Engineering, Peking University, Beijing, 100871, China；Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China；Department of Mathematics, Hong Kong Baptist University, Hong Kong, 999077, China
Stanford Center for Biomedical Informatics Research（斯坦福生物医学信息学研究中心）；Stanford University（斯坦福大学）
The use of machine learning systems to support decision making in healthcare raises questions as to what extent these systems may introduce or exacerbate disparities in care for historically underrepresented and mistreated groups, due to biases implicitly embedded in observational data in electronic health records. To address this problem in the context of clinical risk prediction models, we develop an augmented counterfactual fairness criteria to extend the group fairness criteria of equalized odds to an individual level. We do so by requiring that the same prediction be made for a patient, a…
Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement
Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany（德国癌症研究中心（DKFZ）计算基因组学与系统遗传学部，海德堡，德国）；Helmholtz Information & Data Science School for Health, Germany（德国健康信息与数据科学学院）；Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany（欧洲分子生物学实验室（EMBL）基因组生物学部，海德堡，德国）；Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands（格罗宁根大学医学中心基因学系，格罗宁根，荷兰）；Oncode Institute, Utrecht, The Netherlands（奥诺代码研究所，乌得勒支，荷兰）；KU Leuven, Leuven, Belgium（鲁汶大学，鲁汶，比利时）；Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK（沃里克桑格研究所，沃里克基因组校园，欣斯顿，英国）
When applied to a real-world safety critical system like the power grid, general machine learning methods suffer from expensive training, non-physical solutions, and limited interpretability. To address these challenges for power grids, many recent works have explored the inclusion of grid physics (i.e., domain expertise) into their method design, primarily through including system constraints and technical limits, reducing search space and defining meaningful features in latent space. Yet, there is no general methodology to evaluate the practicality of these approaches in power grid tasks, an…
University of Rochester（罗切斯特大学）；Comcast Applied AI Research（康卡斯特应用人工智能研究院）
With the recent trend of applying machine learning in every aspect of human life, it is important to incorporate fairness into the core of the predictive algorithms. We address the problem of predicting the quality of public speeches while being fair with respect to sensitive attributes of the speakers, e.g. gender and race. We use the TED talks as an input repository of public speeches because it consists of speakers from a diverse community and has a wide outreach. Utilizing the theories of Causal Models, Counterfactual Fairness and state-of-the-art neural language models, we propose a mathe…
