A critical problem in post hoc explainability is the lack of a common foundational goal among methods. For example, some methods are motivated by function approximation, some by game theoretic notions, and some by obtaining clean visualizations. This fragmentation of goals causes not only an inconsistent conceptual understanding of explanations but also the practical challenge of not knowing which method to use when. In this work, we begin to address these challenges by unifying eight popular post hoc explanation methods (LIME, C-LIME, SHAP, Occlusion, Vanilla Gradients, Gradients x Input, SmoothGrad, and Integrated Gradients). We show that these methods all perform local function approximation of the black-box model, differing only in the neighbourhood and loss function used to perform the approximation. This unification enables us to (1) state a no free lunch theorem for explanation methods which demonstrates that no single method can perform optimally across all neighbourhoods, and (2) provide a guiding principle to choose among methods based on faithfulness to the black-box model. We empirically validate these theoretical results using various real-world datasets, model classes, and prediction tasks. By bringing diverse explanation methods into a common framework, this work (1) advances the conceptual understanding of these methods, revealing their shared local function approximation objective, properties, and relation to one another, and (2) guides the use of these methods in practice, providing a principled approach to choose among methods and paving the way for the creation of new ones.

尽管在最近的文献中提出了几种类型的事后解释方法（例如，特征归因方法），但在系统地以有效且透明的方式进行系统基准测试这些方法几乎没有工作。在这里，我们介绍了OpenXai，这是一个全面且可扩展的开源框架，用于评估和基准测试事后解释方法。 OpenXAI由以下关键组件组成：（i）灵活的合成数据生成器以及各种现实世界数据集，预训练的模型和最新功能属性方法的集合，（ii）开源实现22个定量指标，用于评估忠诚，稳定性（稳健性）和解释方法的公平性，以及（iii）有史以来第一个公共XAI XAI排行榜对基准解释。 OpenXAI很容易扩展，因为用户可以轻松地评估自定义说明方法并将其纳入我们的排行榜。总体而言，OpenXAI提供了一种自动化的端到端管道，该管道不仅简化并标准化了事后解释方法的评估，而且还促进了基准这些方法的透明度和可重复性。 OpenXAI数据集和数据加载程序，最先进的解释方法的实现和评估指标以及排行榜，可在https://open-xai.github.io/上公开获得。

由于黑匣子的解释越来越多地用于在高赌注设置中建立模型可信度，重要的是确保这些解释准确可靠。然而，事先工作表明，最先进的技术产生的解释是不一致的，不稳定的，并且提供了对它们的正确性和可靠性的极少了解。此外，这些方法也在计算上效率低下，并且需要显着的超参数调谐。在本文中，我们通过开发一种新的贝叶斯框架来涉及用于产生当地解释以及相关的不确定性来解决上述挑战。我们将本框架实例化以获取贝叶斯版本的石灰和kernelshap，其为特征重要性输出可靠的间隔，捕获相关的不确定性。由此产生的解释不仅使我们能够对其质量进行具体推论（例如，有95％的几率是特征重要性在给定范围内），但也是高度一致和稳定的。我们执行了一个详细的理论分析，可以利用上述不确定性来估计对样品的扰动有多少，以及如何进行更快的收敛。这项工作首次尝试在一次拍摄中通过流行的解释方法解决几个关键问题，从而以计算上有效的方式产生一致，稳定和可靠的解释。具有多个真实世界数据集和用户研究的实验评估表明，提出的框架的功效。

由于事后解释方法越来越多地被利用以在高风险环境中解释复杂的模型，因此确保在包括少数群体在内的各个种群亚组中，所得解释的质量始终高。例如，与与其他性别相关的实例（例如，女性）相关的实例（例如，女性）的说明不应该是与其他性别相关的解释。但是，几乎没有研究能够评估通过最先进的解释方法在输出的解释质量上是否存在这种基于群体的差异。在这项工作中，我们通过启动确定基于群体的解释质量差异的研究来解决上述差距。为此，我们首先概述了构成解释质量以及差异尤其有问题的关键属性。然后，我们利用这些属性提出了一个新的评估框架，该框架可以通过最新方法定量测量解释质量的差异。使用此框架，我们进行了严格的经验分析，以了解是否出现了解释质量的基于小组的差异。我们的结果表明，当所解释的模型复杂且高度非线性时，这种差异更可能发生。此外，我们还观察到某些事后解释方法（例如，综合梯度，外形）更有可能表现出上述差异。据我们所知，这项工作是第一个强调和研究解释质量差异的问题。通过这样做，我们的工作阐明了以前未开发的方式，其中解释方法可能在现实世界决策中引入不公平。

研究人员提出了多种模型解释方法，但目前尚不清楚大多数方法如何相关或何时一种方法比另一种方法更可取。我们研究了文献，发现许多方法都是基于通过删除来解释的共同原理 - 本质上是测量从模型中删除一组特征的影响。这些方法在几个方面有所不同，因此我们为基于删除的解释开发了一个沿三个维度表征每个方法的框架：1）该方法如何删除特征，2）该方法解释的模型行为以及3）方法如何汇总每个方法功能的影响。我们的框架统一了26种现有方法，其中包括几种最广泛使用的方法（Shap，Lime，有意义的扰动，排列测试）。揭露这些方法之间的基本相似性使用户能够推荐使用哪种工具，并为正在进行的模型解释性研究提出了有希望的方向。

众所周知，端到端的神经NLP体系结构很难理解，这引起了近年来为解释性建模的许多努力。模型解释的基本原则是忠诚，即，解释应准确地代表模型预测背后的推理过程。这项调查首先讨论了忠诚的定义和评估及其对解释性的意义。然后，我们通过将方法分为五类来介绍忠实解释的最新进展：相似性方法，模型内部结构的分析，基于反向传播的方法，反事实干预和自我解释模型。每个类别将通过其代表性研究，优势和缺点来说明。最后，我们从它们的共同美德和局限性方面讨论了上述所有方法，并反思未来的工作方向忠实的解释性。对于有兴趣研究可解释性的研究人员，这项调查将为该领域提供可访问且全面的概述，为进一步探索提供基础。对于希望更好地了解自己的模型的用户，该调查将是一项介绍性手册，帮助选择最合适的解释方法。

As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice, there is growing emphasis on building tools and techniques for explaining these black boxes in an interpretable manner. Such explanations are being leveraged by domain experts to diagnose systematic errors and underlying biases of black boxes. In this paper, we demonstrate that post hoc explanations techniques that rely on input perturbations, such as LIME and SHAP, are not reliable. Specifically, we propose a novel scaffolding technique that effectively hides the biases of any given classifier by allowing an adversarial entity to craft an arbitrary desired explanation. Our approach can be used to scaffold any biased classifier in such a way that its predictions on the input data distribution still remain biased, but the post hoc explanations of the scaffolded classifier look innocuous. Using extensive evaluation with multiple real world datasets (including COMPAS), we demonstrate how extremely biased (racist) classifiers crafted by our framework can easily fool popular explanation techniques such as LIME and SHAP into generating innocuous explanations which do not reflect the underlying biases. CCS CONCEPTS• Computing methodologies → Machine learning; Supervised learning by classification; • Human-centered computing → Interactive systems and tools.

Explainability has been widely stated as a cornerstone of the responsible and trustworthy use of machine learning models. With the ubiquitous use of Deep Neural Network (DNN) models expanding to risk-sensitive and safety-critical domains, many methods have been proposed to explain the decisions of these models. Recent years have also seen concerted efforts that have shown how such explanations can be distorted (attacked) by minor input perturbations. While there have been many surveys that review explainability methods themselves, there has been no effort hitherto to assimilate the different methods and metrics proposed to study the robustness of explanations of DNN models. In this work, we present a comprehensive survey of methods that study, understand, attack, and defend explanations of DNN models. We also present a detailed review of different metrics used to evaluate explanation methods, as well as describe attributional attack and defense methods. We conclude with lessons and take-aways for the community towards ensuring robust explanations of DNN model predictions.

越来越多的电子健康记录（EHR）数据和深度学习技术进步的越来越多的可用性（DL）已经引发了在开发基于DL的诊断，预后和治疗的DL临床决策支持系统中的研究兴趣激增。尽管承认医疗保健的深度学习的价值，但由于DL的黑匣子性质，实际医疗环境中进一步采用的障碍障碍仍然存在。因此，有一个可解释的DL的新兴需求，它允许最终用户评估模型决策，以便在采用行动之前知道是否接受或拒绝预测和建议。在这篇综述中，我们专注于DL模型在医疗保健中的可解释性。我们首先引入深入解释性的方法，并作为该领域的未来研究人员或临床从业者的方法参考。除了这些方法的细节之外，我们还包括对这些方法的优缺点以及它们中的每个场景都适合的讨论，因此感兴趣的读者可以知道如何比较和选择它们供使用。此外，我们讨论了这些方法，最初用于解决一般域问题，已经适应并应用于医疗保健问题以及如何帮助医生更好地理解这些数据驱动技术。总的来说，我们希望这项调查可以帮助研究人员和从业者在人工智能（AI）和临床领域了解我们为提高其DL模型的可解释性并相应地选择最佳方法。

Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches.

即使有效，模型的使用也必须伴随着转换数据的各个级别的理解（上游和下游）。因此，需求增加以定义单个数据与算法可以根据其分析可以做出的选择（例如，一种产品或一种促销报价的建议，或代表风险的保险费率）。模型用户必须确保模型不会区分，并且也可以解释其结果。本文介绍了模型解释的重要性，并解决了模型透明度的概念。在保险环境中，它专门说明了如何使用某些工具来强制执行当今可以利用机器学习的精算模型的控制。在一个简单的汽车保险中损失频率估计的示例中，我们展示了一些解释性方法的兴趣，以适应目标受众的解释。

基于Shapley值的功能归因在解释机器学习模型中很受欢迎。但是，从理论和计算的角度来看，它们的估计是复杂的。我们将这种复杂性分解为两个因素：（1）〜删除特征信息的方法，以及（2）〜可拖动估计策略。这两个因素提供了一种天然镜头，我们可以更好地理解和比较24种不同的算法。基于各种特征删除方法，我们描述了多种类型的Shapley值特征属性和计算每个类型的方法。然后，基于可进行的估计策略，我们表征了两个不同的方法家族：模型 - 不合时宜的和模型特定的近似值。对于模型 - 不合稳定的近似值，我们基准了广泛的估计方法，并将其与Shapley值的替代性但等效的特征联系起来。对于特定于模型的近似值，我们阐明了对每种方法的线性，树和深模型的障碍至关重要的假设。最后，我们确定了文献中的差距以及有希望的未来研究方向。

本文研究了与可解释的AI（XAI）实践有关的两个不同但相关的问题。机器学习（ML）在金融服务中越来越重要，例如预批准，信用承销，投资以及各种前端和后端活动。机器学习可以自动检测培训数据中的非线性和相互作用，从而促进更快，更准确的信用决策。但是，机器学习模型是不透明的，难以解释，这是建立可靠技术所需的关键要素。该研究比较了各种机器学习模型，包括单个分类器（逻辑回归，决策树，LDA，QDA），异质集合（Adaboost，随机森林）和顺序神经网络。结果表明，整体分类器和神经网络的表现优于表现。此外，使用基于美国P2P贷款平台Lending Club提供的开放式访问数据集评估了两种先进的事后不可解释能力 - 石灰和外形来评估基于ML的信用评分模型。对于这项研究，我们还使用机器学习算法来开发新的投资模型，并探索可以最大化盈利能力同时最大程度地降低风险的投资组合策略。

Post-hoc explanation methods have become increasingly depended upon for understanding black-box classifiers in high-stakes applications, precipitating a need for reliable explanations. While numerous explanation methods have been proposed, recent works have shown that many existing methods can be inconsistent or unstable. In addition, high-performing classifiers are often highly nonlinear and can exhibit complex behavior around the decision boundary, leading to brittle or misleading local explanations. Therefore, there is an impending need to quantify the uncertainty of such explanation methods in order to understand when explanations are trustworthy. We introduce a novel uncertainty quantification method parameterized by a Gaussian Process model, which combines the uncertainty approximation of existing methods with a novel geodesic-based similarity which captures the complexity of the target black-box decision boundary. The proposed framework is highly flexible; it can be used with any black-box classifier and feature attribution method to amortize uncertainty estimates for explanations. We show theoretically that our proposed geodesic-based kernel similarity increases with the complexity of the decision boundary. Empirical results on multiple tabular and image datasets show that our decision boundary-aware uncertainty estimate improves understanding of explanations as compared to existing methods.

在本文中，我们提出了一种新的可解释性形式主义，旨在阐明测试集的每个输入变量如何影响机器学习模型的预测。因此，我们根据训练有素的机器学习决策规则提出了一个群体的解释性形式，它们是根据其对输入变量分布的可变性的反应。为了强调每个输入变量的影响，这种形式主义使用信息理论框架，该框架量化了基于熵投影的所有输入输出观测值的影响。因此，这是第一个统一和模型不可知的形式主义，使数据科学家能够解释输入变量之间的依赖性，它们对预测错误的影响以及它们对输出预测的影响。在大型样本案例中提供了熵投影的收敛速率。最重要的是，我们证明，计算框架中的解释具有低算法的复杂性，使其可扩展到现实生活中的大数据集。我们通过解释通过在各种数据集上使用XGBoost，随机森林或深层神经网络分类器（例如成人收入，MNIST，CELEBA，波士顿住房，IRIS以及合成的）上使用的复杂决策规则来说明我们的策略。最终，我们明确了基于单个观察结果的解释性策略石灰和摇摆的差异。可以通过使用自由分布的Python工具箱https://gems-ai.aniti.fr/来复制结果。

越来越多的研究进行了人类主题评估，以研究为用户提供机器学习模型的解释是否可以帮助他们制定实际现实世界中的用例。但是，运行的用户研究具有挑战性且昂贵，因此每个研究通常只评估有限的不同设置，例如，研究通常只评估一些任意选择的解释方法。为了应对这些挑战和援助用户研究设计，我们介绍了用用例的模拟评估（Simevals）。 SIMEVALS涉及培训算法剂，以输入信息内容（例如模型解释），这些信息内容将在人类学科研究中提交给每个参与者，以预测感兴趣的用例的答案。算法代理的测试集精度提供了衡量下游用例信息内容的预测性。我们对三种现实世界用例（正向模拟，模型调试和反事实推理）进行全面评估，以证明Simevals可以有效地确定哪种解释方法将为每个用例提供帮助。这些结果提供了证据表明，Simevals可用于有效筛选一组重要的用户研究设计决策，例如在进行潜在昂贵的用户研究之前，选择应向用户提供哪些解释。

In the last years many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness sometimes at the cost of scarifying accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, delineating explicitly or implicitly its own definition of interpretability and explanation. The aim of this paper is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.

Explainable artificial intelligence is proposed to provide explanations for reasoning performed by an Artificial Intelligence. There is no consensus on how to evaluate the quality of these explanations, since even the definition of explanation itself is not clear in the literature. In particular, for the widely known Local Linear Explanations, there are qualitative proposals for the evaluation of explanations, although they suffer from theoretical inconsistencies. The case of image is even more problematic, where a visual explanation seems to explain a decision while detecting edges is what it really does. There are a large number of metrics in the literature specialized in quantitatively measuring different qualitative aspects so we should be able to develop metrics capable of measuring in a robust and correct way the desirable aspects of the explanations. In this paper, we propose a procedure called REVEL to evaluate different aspects concerning the quality of explanations with a theoretically coherent development. This procedure has several advances in the state of the art: it standardizes the concepts of explanation and develops a series of metrics not only to be able to compare between them but also to obtain absolute information regarding the explanation itself. The experiments have been carried out on image four datasets as benchmark where we show REVEL's descriptive and analytical power.

Some recent works observed the instability of post-hoc explanations when input side perturbations are applied to the model. This raises the interest and concern in the stability of post-hoc explanations. However, the remaining question is: is the instability caused by the neural network model or the post-hoc explanation method? This work explores the potential source that leads to unstable post-hoc explanations. To separate the influence from the model, we propose a simple output probability perturbation method. Compared to prior input side perturbation methods, the output probability perturbation method can circumvent the neural model's potential effect on the explanations and allow the analysis on the explanation method. We evaluate the proposed method with three widely-used post-hoc explanation methods (LIME (Ribeiro et al., 2016), Kernel Shapley (Lundberg and Lee, 2017a), and Sample Shapley (Strumbelj and Kononenko, 2010)). The results demonstrate that the post-hoc methods are stable, barely producing discrepant explanations under output probability perturbations. The observation suggests that neural network models may be the primary source of fragile explanations.

人工智能（AI）和机器学习（ML）在网络安全挑战中的应用已在行业和学术界的吸引力，部分原因是对关键系统（例如云基础架构和政府机构）的广泛恶意软件攻击。入侵检测系统（IDS）使用某些形式的AI，由于能够以高预测准确性处理大量数据，因此获得了广泛的采用。这些系统托管在组织网络安全操作中心（CSOC）中，作为一种防御工具，可监视和检测恶意网络流，否则会影响机密性，完整性和可用性（CIA）。 CSOC分析师依靠这些系统来决定检测到的威胁。但是，使用深度学习（DL）技术设计的IDS通常被视为黑匣子模型，并且没有为其预测提供理由。这为CSOC分析师造成了障碍，因为他们无法根据模型的预测改善决策。解决此问题的一种解决方案是设计可解释的ID（X-IDS）。这项调查回顾了可解释的AI（XAI）的最先进的ID，目前的挑战，并讨论了这些挑战如何涉及X-ID的设计。特别是，我们全面讨论了黑匣子和白盒方法。我们还在这些方法之间的性能和产生解释的能力方面提出了权衡。此外，我们提出了一种通用体系结构，该建筑认为人类在循环中，该架构可以用作设计X-ID时的指南。研究建议是从三个关键观点提出的：需要定义ID的解释性，需要为各种利益相关者量身定制的解释以及设计指标来评估解释的需求。