最先进的实体匹配（EM）方法很难解释，并且为EM带来可解释的AI具有重要的价值。不幸的是，大多数流行的解释性方法无法开箱即用，需要适应。在本文中，我们确定了将本地事后特征归因方法应用于实体匹配的三个挑战：跨记录的交互作用，不匹配的解释和灵敏度变化。我们提出了新颖的模型 - 静态和模式 - 富含模型的方法柠檬柠檬，该方法通过（i）产生双重解释来避免交叉记录的互动效果来应对所有三个挑战，（ii）介绍了归因潜力的新颖概念，以解释两个记录如何能够拥有如何具有匹配，（iii）自动选择解释粒度以匹配匹配器和记录对的灵敏度。公共数据集上的实验表明，所提出的方法更忠实于匹配器，并且在帮助用户了解匹配器的决策边界的工作中比以前的工作更具忠诚度。此外，用户研究表明，与标准的解释相比石灰的适应。

在人类循环机器学习应用程序的背景下，如决策支持系统，可解释性方法应在不使用户等待的情况下提供可操作的见解。在本文中，我们提出了加速的模型 - 不可知论解释（ACME），一种可解释的方法，即在全球和本地层面迅速提供特征重要性分数。可以将acme应用于每个回归或分类模型的后验。 ACME计算功能排名不仅提供了一个什么，但它还提供了一个用于评估功能值的变化如何影响模型预测的原因 - 如果分析工具。我们评估了综合性和现实世界数据集的建议方法，同时也与福芙添加剂解释（Shap）相比，我们制作了灵感的方法，目前是最先进的模型无关的解释性方法。我们在生产解释的质量方面取得了可比的结果，同时急剧减少计算时间并为全局和局部解释提供一致的可视化。为了促进该领域的研究，为重复性，我们还提供了一种存储库，其中代码用于实验。

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。

Interpretability provides a means for humans to verify aspects of machine learning (ML) models and empower human+ML teaming in situations where the task cannot be fully automated. Different contexts require explanations with different properties. For example, the kind of explanation required to determine if an early cardiac arrest warning system is ready to be integrated into a care setting is very different from the type of explanation required for a loan applicant to help determine the actions they might need to take to make their application successful. Unfortunately, there is a lack of standardization when it comes to properties of explanations: different papers may use the same term to mean different quantities, and different terms to mean the same quantity. This lack of a standardized terminology and categorization of the properties of ML explanations prevents us from both rigorously comparing interpretable machine learning methods and identifying what properties are needed in what contexts. In this work, we survey properties defined in interpretable machine learning papers, synthesize them based on what they actually measure, and describe the trade-offs between different formulations of these properties. In doing so, we enable more informed selection of task-appropriate formulations of explanation properties as well as standardization for future work in interpretable machine learning.

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。

Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one.In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally around the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. We demonstrate the flexibility of these methods by explaining different models for text (e.g. random forests) and image classification (e.g. neural networks). We show the utility of explanations via novel experiments, both simulated and with human subjects, on various scenarios that require trust: deciding if one should trust a prediction, choosing between models, improving an untrustworthy classifier, and identifying why a classifier should not be trusted.

我们提出了一种可解释的关系提取方法，通过共同训练这两个目标来减轻概括和解释性之间的张力。我们的方法使用多任务学习体系结构，该体系结构共同训练分类器以进行关系提取，并在解释关系分类器的决策的关系中标记单词的序列模型。我们还将模型输出转换为规则，以将全局解释带入这种方法。使用混合策略对此序列模型进行训练：有监督，当可获得预先存在的模式的监督时，另外还要半监督。在后一种情况下，我们将序列模型的标签视为潜在变量，并学习最大化关系分类器性能的最佳分配。我们评估了两个数据集中的提议方法，并表明序列模型提供了标签，可作为关系分类器决策的准确解释，并且重要的是，联合培训通常可以改善关系分类器的性能。我们还评估了生成的规则的性能，并表明新规则是手动规则的重要附加功能，并使基于规则的系统更接近神经模型。

我们在电影推荐任务上评估了两种流行的本地解释性技术，即石灰和外形。我们发现，这两种方法的行为取决于数据集的稀疏性。在数据集的密集段中，石灰的表现要好，而在稀疏段中，shap的表现更好。我们将这种差异追溯到石灰和摇动​​基础估计量的不同偏差变化特征。我们发现，与石灰相比，SHAP在数据的稀疏段中表现出较低的方差。我们将这种较低的差异归因于Shap和Lime中缺少的完整性约束属性。该约束是正规化器，因此增加了Shap估计器的偏差，但会降低其差异，从而导致良好的偏见差异权衡，尤其是在高稀疏数据设置中。有了这个见解，我们将相同的约束引入石灰，并制定了一个新颖的局部解释框架，称为完整性约束的石灰（攀爬），比石灰优于石灰，速度比Shap更快。

除了机器学习（ML）模型的令人印象深刻的预测力外，最近还出现了解释方法，使得能够解释诸如深神经网络的复杂非线性学习模型。获得更好的理解尤其重要。对于安全 - 关键的ML应用或医学诊断等。虽然这种可解释的AI（XAI）技术对分类器达到了重大普及，但到目前为止对XAI的重点进行了很少的关注（Xair）。在这篇综述中，我们澄清了XAI对回归和分类任务的基本概念差异，为Xair建立了新的理论见解和分析，为Xair提供了真正的实际回归问题的示范，最后讨论了该领域仍然存在的挑战。

与此同时，在可解释的人工智能（XAI）的研究领域中，已经开发了各种术语，动机，方法和评估标准。随着XAI方法的数量大大增长，研究人员以及从业者以及从业者需要一种方法：掌握主题的广度，比较方法，并根据特定用例所需的特征选择正确的XAI方法语境。在文献中，可以找到许多不同细节水平和深度水平的XAI方法分类。虽然他们经常具有不同的焦点，但它们也表现出许多重叠点。本文统一了这些努力，并提供了XAI方法的分类，这是关于目前研究中存在的概念的概念。在结构化文献分析和元研究中，我们识别并审查了XAI方法，指标和方法特征的50多个最引用和最新的调查。总结在调查调查中，我们将文章的术语和概念合并为统一的结构化分类。其中的单一概念总计超过50个不同的选择示例方法，我们相应地分类。分类学可以为初学者，研究人员和从业者提供服务作为XAI方法特征和方面的参考和广泛概述。因此，它提供了针对有针对性的，用例导向的基础和上下文敏感的未来研究。

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。

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.

由于算法预测对人类的影响增加，模型解释性已成为机器学习（ML）的重要问题。解释不仅可以帮助用户了解为什么ML模型做出某些预测，还可以帮助用户了解这些预测如何更改。在本论文中，我们研究了从三个有利位置的ML模型的解释性：算法，用户和教学法，并为解释性问题贡献了一些新颖的解决方案。

如今，人工智能（AI）已成为临床和远程医疗保健应用程序的基本组成部分，但是最佳性能的AI系统通常太复杂了，无法自我解释。可解释的AI（XAI）技术被定义为揭示系统的预测和决策背后的推理，并且在处理敏感和个人健康数据时，它们变得更加至关重要。值得注意的是，XAI并未在不同的研究领域和数据类型中引起相同的关注，尤其是在医疗保健领域。特别是，许多临床和远程健康应用程序分别基于表格和时间序列数据，而XAI并未在这些数据类型上进行分析，而计算机视觉和自然语言处理（NLP）是参考应用程序。为了提供最适合医疗领域表格和时间序列数据的XAI方法的概述，本文提供了过去5年中文献的审查，说明了生成的解释的类型以及为评估其相关性所提供的努力和质量。具体而言，我们确定临床验证，一致性评估，客观和标准化质量评估以及以人为本的质量评估作为确保最终用户有效解释的关键特征。最后，我们强调了该领域的主要研究挑战以及现有XAI方法的局限性。

Deep Learning and Machine Learning based models have become extremely popular in text processing and information retrieval. However, the non-linear structures present inside the networks make these models largely inscrutable. A significant body of research has focused on increasing the transparency of these models. This article provides a broad overview of research on the explainability and interpretability of natural language processing and information retrieval methods. More specifically, we survey approaches that have been applied to explain word embeddings, sequence modeling, attention modules, transformers, BERT, and document ranking. The concluding section suggests some possible directions for future research on this topic.

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.

Automated Machine Learning-based systems' integration into a wide range of tasks has expanded as a result of their performance and speed. Although there are numerous advantages to employing ML-based systems, if they are not interpretable, they should not be used in critical, high-risk applications where human lives are at risk. To address this issue, researchers and businesses have been focusing on finding ways to improve the interpretability of complex ML systems, and several such methods have been developed. Indeed, there are so many developed techniques that it is difficult for practitioners to choose the best among them for their applications, even when using evaluation metrics. As a result, the demand for a selection tool, a meta-explanation technique based on a high-quality evaluation metric, is apparent. In this paper, we present a local meta-explanation technique which builds on top of the truthfulness metric, which is a faithfulness-based metric. We demonstrate the effectiveness of both the technique and the metric by concretely defining all the concepts and through experimentation.

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.

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。

Artificial intelligence(AI) systems based on deep neural networks (DNNs) and machine learning (ML) algorithms are increasingly used to solve critical problems in bioinformatics, biomedical informatics, and precision medicine. However, complex DNN or ML models that are unavoidably opaque and perceived as black-box methods, may not be able to explain why and how they make certain decisions. Such black-box models are difficult to comprehend not only for targeted users and decision-makers but also for AI developers. Besides, in sensitive areas like healthcare, explainability and accountability are not only desirable properties of AI but also legal requirements -- especially when AI may have significant impacts on human lives. Explainable artificial intelligence (XAI) is an emerging field that aims to mitigate the opaqueness of black-box models and make it possible to interpret how AI systems make their decisions with transparency. An interpretable ML model can explain how it makes predictions and which factors affect the model's outcomes. The majority of state-of-the-art interpretable ML methods have been developed in a domain-agnostic way and originate from computer vision, automated reasoning, or even statistics. Many of these methods cannot be directly applied to bioinformatics problems, without prior customization, extension, and domain adoption. In this paper, we discuss the importance of explainability with a focus on bioinformatics. We analyse and comprehensively overview of model-specific and model-agnostic interpretable ML methods and tools. Via several case studies covering bioimaging, cancer genomics, and biomedical text mining, we show how bioinformatics research could benefit from XAI methods and how they could help improve decision fairness.