In decision-making processes, stakeholders often rely on counterfactual explanations, which provide suggestions about what should be changed in the queried instance to alter the outcome of an AI system. However, generating these explanations for multivariate time series presents challenges due to their complex, multi-dimensional nature. Traditional Nearest Unlike Neighbor-based methods typically substitute subsequences in a queried time series with influential subsequences from an NUN, which is not always realistic in real-world scenarios due to the rigid direct substitution. Counterfactual with Residual Generative Adversarial Networks-based methods aim to address this by learning from the distribution of observed data to generate synthetic counterfactual explanations. However, these methods primarily focus on minimizing the cost from the queried time series to the counterfactual explanations and often neglect the importance of distancing the counterfactual explanation from the decision boundary. This oversight can result in explanations that no longer qualify as counterfactual if minor changes occur within the model. To generate a more robust counterfactual explanation, we introduce TriShGAN, under the CounteRGAN framework enhanced by the incorporation of triplet loss. This unsupervised learning approach uses distance metric learning to encourage the counterfactual explanations not only to remain close to the queried time series but also to capture the feature distribution of the instance with the desired outcome, thereby achieving a better balance between minimal cost and robustness. Additionally, we integrate a Shapelet Extractor that strategically selects the most discriminative parts of the high-dimensional queried time series to enhance the sparsity of counterfactual explanation and efficiency of the training process.
Hertie Institute for Clinical Brain Research（赫蒂临床脑研究所）；Centre for Integrative Neuroscience（整合神经科学中心）；University of Tübingen（图宾根大学）
Since neural networks play an increasingly important role in critical sectors, explaining network predictions has become a key research topic. Counterfactual explanations can help to understand why classifier models decide for particular class assignments and, moreover, how the respective input samples would have to be modified such that the class prediction changes. Previous approaches mainly focus on image and tabular data. In this work we propose SPARCE, a generative adversarial network (GAN) architecture that generates SPARse Counterfactual Explanations for multivariate time series. Our ap…
DiCoFlex: Model-agnostic diverse counterfactuals with flexible control
Wrocław University of Science and Technology（沃拉夫大学科学与技术大学）；Faculty of Mathematics and Computer Science, Jagiellonian University（杰尔吉扬大学数学与计算机科学学院）；Doctoral School of Exact and Natural Sciences, Jagiellonian University（杰尔吉扬大学精确与自然科学博士学院）；Tooploox Sp. z o.o.（Tooploox 公司）
Insight SFI Centre for Data Analytics（Insight SFI数据分析中心）；VistaMilk SFI Research Centre（VistaMilk SFI研究中心）；University College Dublin（都柏林大学）
Counterfactual explanations provide a potentially significant solution to the Explainable AI (XAI) problem, but good, native counterfactuals have been shown to rarely occur in most datasets. Hence, the most popular methods generate synthetic counterfactuals using blind perturbation. However, such methods have several shortcomings: the resulting counterfactuals (i) may not be valid data-points (they often use features that do not naturally occur), (ii) may lack the sparsity of good counterfactuals (if they modify too many features), and (iii) may lack diversity (if the generated counterfactuals…
In Artificial Intelligence, interpreting the results of a Machine Learning technique often termed as a black box is a difficult task. A counterfactual explanation of a particular "black box" attempts to find the smallest change to the input values that modifies the prediction to a particular output, other than the original one. In this work we formulate the problem of finding a counterfactual explanation as an optimization problem. We propose a new "sparsity algorithm" which solves the optimization problem, while also maximizing the sparsity of the counterfactual explanation. We apply the spar…
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…
In this paper we suggest NICE: a new algorithm to generate counterfactual explanations for heterogeneous tabular data. The design of our algorithm specifically takes into account algorithmic requirements that often emerge in real-life deployments: (1) the ability to provide an explanation for all predictions, (2) being able to handle any classification model (also non-differentiable ones), and (3) being efficient in run time. More specifically, our approach exploits information from a nearest unlike neighbour to speed up the search process, by iteratively introducing feature values from this n…
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Amira Learning（Amira学习）；CognitiveScale（认知规模）
Counterfactual explanations have substantially increased in popularity in the past few years as a useful human-centric way of understanding individual black-box model predictions. While several properties desired of high-quality counterfactuals have been identified in the literature, three crucial concerns: the speed of explanation generation, robustness/sensitivity and succinctness of explanations (sparsity) have been relatively unexplored. In this paper, we present FASTER-CE: a novel set of algorithms to generate fast, sparse, and robust counterfactual explanations. The key idea is to effici…
We present a new method for counterfactual explanations (CFEs) based on Bayesian optimisation that applies to both classification and regression models. Our method is a globally convergent search algorithm with support for arbitrary regression models and constraints like feature sparsity and actionable recourse, and furthermore can answer multiple counterfactual questions in parallel while learning from previous queries. We formulate CFE search for regression models in a rigorous mathematical framework using differentiable potentials, which resolves robustness issues in threshold-based objecti…
Actionable and diverse counterfactual explanations incorporating domain knowledge and plausibility constraints
Faculty of Physics, Astronomy and Applied Computer Science, Institute of Applied Computer Science, Jagiellonian Human-Centered AI Lab（物理、天文与应用计算机科学学院，应用计算机科学研究所，雅盖隆人机中心AI实验室）
CounterFlowNet: From Minimal Changes to Meaningful Counterfactual Explanations
Wrocław University of Science and Technology（沃拉布大学科学与技术大学）；Faculty of Mathematics and Computer Science（数学与计算机科学系）；Jagiellonian University（雅盖隆大学）；Doctoral School of Exact and Natural Sciences（精确与自然科学博士学校）
Class-Aware Reinforcement Learning for Counterfactual Explanation Generation
Institute of Business Administration Karachi（卡拉奇工商管理学院）；Commonwealth Bank of Australia（澳大利亚联邦银行）；Stanford University（斯坦福大学）；University of New South Wales（新南威尔士大学）
Utah State University（犹他州立大学）；New Mexico State University（新墨西哥州立大学）
As machine learning and deep learning models have become highly prevalent in a multitude of domains, the main reservation in their adoption for decision-making processes is their black-box nature. The Explainable Artificial Intelligence (XAI) paradigm has gained a lot of momentum lately due to its ability to reduce models opacity. XAI methods have not only increased stakeholders' trust in the decision process but also helped developers ensure its fairness. Recent efforts have been invested in creating transparent models and post-hoc explanations. However, fewer methods have been developed for …
CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests
Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；LMU Munich（慕尼黑大学）；Leibniz Institute for Prevention Research & Epidemiology – BIPS（莱布尼茨预防研究与流行病学研究所）；University of Bremen（不来梅大学）；University of Tübingen（蒂宾根大学）；University of Copenhagen（哥本哈根大学）
MASCOTS: Model-Agnostic Symbolic COunterfactual explanations for Time Series
Warsaw University of Technology（华沙理工大学）；University of Warsaw（华沙大学）；University of Pisa（比萨大学）；ISTI-CNR（意大利国家研究委员会信息科学与技术研究所）；KP Labs（KP实验室）；European Space Operations Centre（欧洲空间操作中心）；ETH Zürich（苏黎世联邦理工学院）
Physics-Guided Counterfactual Explanations for Large-Scale Multivariate Time Series: Application in Scalable and Interpretable SEP Event Prediction
Dept. of Computer Science Georgia State University Atlanta, GA, USA（计算机科学系 佐治亚州立大学）；Dept. of Computer Science California State University Fullerton, CA, USA（计算机科学系 加州州立大学Fullerton分校）
Perfect Counterfactuals in Imperfect Worlds: Modelling Noisy Implementation of Actions in Sequential Algorithmic Recourse
ARC Centre of Excellence for Automated Decision-Making and Society（自动化决策与社会卓越中心）；School of Computing Technologies, RMIT University（计算技术学院，拉筹纳斯大学）；Department of Computer Science, ETH Zurich（计算机科学系，苏黎世联邦理工学院）
