Recent advancements in AI have coincided with ever-increasing efforts in the research community to investigate, classify and evaluate various methods aimed at making AI models explainable. However, most of existing attempts present a method-centric view of eXplainable AI (XAI) which is typically meaningful only for domain experts. There is an apparent lack of a robust qualitative and quantitative performance framework that evaluates the suitability of explanations for different types of users. We survey relevant efforts, and then, propose a unified, inclusive and user-centred taxonomy for XAI based on the principles of General System's Theory, which serves us as a basis for evaluating the appropriateness of XAI approaches for all user types, including both developers and end users.
University of Genoa（热那亚大学）；Italian Institute of Technology（意大利理工学院）
State of the art Artificial Intelligence (AI) techniques have reached an impressive complexity. Consequently, researchers are discovering more and more methods to use them in real-world applications. However, the complexity of such systems requires the introduction of methods that make those transparent to the human user. The AI community is trying to overcome the problem by introducing the Explainable AI (XAI) field, which is tentative to make AI algorithms less opaque. However, in recent years, it became clearer that XAI is much more than a computer science problem: since it is about communi…
This paper proposes an alternative approach to the basic taxonomy of explanations produced by explainable artificial intelligence techniques. Methods of Explainable Artificial Intelligence (XAI) were developed to answer the question why a certain prediction or estimation was made, preferably in terms easy to understand by the human agent. XAI taxonomies proposed in the literature mainly concentrate their attention on distinguishing explanations with respect to involving the human agent, which makes it complicated to provide a more mathematical approach to distinguish and compare different expl…
In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation criteria have been developed within the research field of explainable artificial intelligence (XAI). With the amount of XAI methods vastly growing, a taxonomy of methods is needed by researchers as well as practitioners: To grasp the breadth of the topic, compare methods, and to select the right XAI method based on traits required by a specific use-case context. Many taxonomies for XAI methods of varying level of detail and depth can be found in the literature. While they often have a different focus, the…
Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations
Indiana University School of Medicine（印第安纳大学医学院）；Indiana University Melvin and Bren Simon Comprehensive Cancer Center（印第安纳大学梅尔文与布伦·西蒙综合癌症中心）；University of Warwick（华威大学）；Emory University（埃默里大学）；Radboud University Medical Center（拉德堡德大学医学中心）；Singapore General Hospital（新加坡中央医院）
On the Design and Evaluation of Human-centered Explainable AI Systems: A Systematic Review and Taxonomy
Dresden University of Technology（德累斯顿技术大学）；Charité Berlin（柏林夏里特医学院）；Department Speculative Transformation（推测转化部门）；Chair of Engineering Psychology and Applied Cognitive Research（工程心理学与应用认知研究教授职位）
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks
As artificial intelligence (AI) systems become increasingly complex and ubiquitous, these systems will be responsible for making decisions that directly affect individuals and society as a whole. Such decisions will need to be justified due to ethical concerns as well as trust, but achieving this has become difficult due to the `black-box' nature many AI models have adopted. Explainable AI (XAI) can potentially address this problem by explaining its actions, decisions and behaviours of the system to users. However, much research in XAI is done in a vacuum using only the researchers' intuition …
DISI, University of Bologna（博洛尼亚大学信息科学与工程系）；CIRSFID-AI, University of Bologna（博洛尼亚大学CIRSFID-AI）
The European Union (EU) through the High-Level Expert Group on Artificial Intelligence (AI-HLEG) and the General Data Protection Regulation (GDPR) has recently posed an interesting challenge to the eXplainable AI (XAI) community, by demanding a more user-centred approach to explain Automated Decision-Making systems (ADMs). Looking at the relevant literature, XAI is currently focused on producing explainable software and explanations that generally follow an approach we could term One-Size-Fits-All, that is unable to meet a requirement of centring on user needs. One of the causes of this limit …
Nowadays, deep neural networks are widely used in mission critical systems such as healthcare, self-driving vehicles, and military which have direct impact on human lives. However, the black-box nature of deep neural networks challenges its use in mission critical applications, raising ethical and judicial concerns inducing lack of trust. Explainable Artificial Intelligence (XAI) is a field of Artificial Intelligence (AI) that promotes a set of tools, techniques, and algorithms that can generate high-quality interpretable, intuitive, human-understandable explanations of AI decisions. In additi…
Berlin University of Applied Sciences（柏林应用科学大学）；Einstein Center Digital Future（爱因斯坦数字未来中心）
Insects are a crucial part of our ecosystem. Sadly, in the past few decades, their numbers have worryingly decreased. In an attempt to gain a better understanding of this process and monitor the insects populations, Deep Learning may offer viable solutions. However, given the breadth of their taxonomy and the typical hurdles of fine grained analysis, such as high intraclass variability compared to low interclass variability, insect classification remains a challenging task. There are few benchmark datasets, which impedes rapid development of better AI models. The annotation of rare species tra…
Explainable artificial intelligence (XAI) seeks to produce explanations for those machine learning methods which are deemed opaque. However, there is considerable disagreement about what this means and how to achieve it. Authors disagree on what should be explained (topic), to whom something should be explained (stakeholder), how something should be explained (instrument), and why something should be explained (goal). In this paper, I employ insights from means-end epistemology to structure the field. According to means-end epistemology, different means ought to be rationally adopted to achiev…
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction
University of Gondar（贡德尔大学）；College of Engineering and Applied Science, University of Colorado, Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校工程与应用科学学院）；Can Tho University（芹苴大学）；Ewha Womans University（梨花女子大学）；University of Colorado, Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）
TECNALIA；ENSTA, Institute Polytechnique Paris（巴黎综合理工学院ENSTA）；INRIA（法国国家信息与自动化研究所）；University of the Basque Country (UPV/EHU)（巴斯克大学（UPV/EHU））；Basque Center for Applied Mathematics (BCAM)（巴斯克应用数学中心（BCAM））；Segula Technologies（Segula科技公司）；Sorbonne Universitè（索邦大学）；DaSCI Andalusian Institute of Data Science and Computational Intelligence, University of Granada（格拉纳达大学DaSCI安达卢西亚数据科学与计算智能研究所）；Telefonica（西班牙电信公司）
In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview exa…
Towards User-Focused Research in Training Data Attribution for Human-Centered Explainable AI
Tübingen AI Center, University of Tübingen Germany（图宾根人工智能中心、图宾根大学德国）；Fraunhofer Heinrich Hertz Institute Germany（弗劳恩霍夫海因里希·赫兹研究所德国）；Information and Interaction Design, Yonsei University South Korea（信息与交互设计，延世大学韩国）
Applied Artificial Intelligence Institute (A2I2)（应用人工智能研究所（A2I2））；Deakin University（迪肯大学）
Deep learning has become popular because of its potential to achieve high accuracy in prediction tasks. However, accuracy is not always the only goal of statistical modelling, especially for models developed as part of scientific research. Rather, many scientific models are developed to facilitate scientific discovery, by which we mean to abstract a human-understandable representation of the natural world. Unfortunately, the opacity of deep neural networks limit their role in scientific discovery, creating a new demand for models that are transparently interpretable. This article is a field gu…
The increasingly widespread application of AI models motivates increased demand for explanations from a variety of stakeholders. However, this demand is ambiguous because there are many types of 'explanation' with different evaluative criteria. In the spirit of pluralism, I chart a taxonomy of types of explanation and the associated XAI methods that can address them. When we look to expose the inner mechanisms of AI models, we develop Diagnostic-explanations. When we seek to render model output understandable, we produce Explication-explanations. When we wish to form stable generalizations of …
Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review
School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Institute of Automotive Engineering, Technical University (TU) of Darmstadt（达姆施塔特工业大学汽车工程研究所）；School of Engineering and Physical Sciences, Heriot-Watt University（赫瑞瓦特大学工程与物理科学学院）
Explainable Artificial Intelligence Techniques for Interpretation of Food Models: a Review
Department of Mathematics, Informatics, and Geosciences University of Trieste（数学、信息学与地质科学系特里斯特大学）；Department of Food Engineering and Technology University of Campinas（食品工程与技术系坎皮纳斯大学）；Faculty of Technology Policy Management University of Technology, Delft（技术政策管理系技术大学，代尔夫特）；ASAC s.r.l., Cessalto (TV), Italy（ASAC公司，Cessalto（TV），意大利）；Department of Food Engineering and Technology, University of Campinas（食品工程与技术系坎皮纳斯大学）
Explainable AI (XAI) is paramount in industry-grade AI; however existing methods fail to address this necessity, in part due to a lack of standardisation of explainability methods. The purpose of this paper is to offer a perspective on the current state of the area of explainability, and to provide novel definitions for Explainability and Interpretability to begin standardising this area of research. To do so, we provide an overview of the literature on explainability, and of the existing methods that are already implemented. Finally, we offer a tentative taxonomy of the different explainabili…
A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms
Heriot-Watt University（赫瑞瓦特大学）；Volkswagen Group（大众集团）；University of Pavia（帕维亚大学）；European University Institute（欧洲大学学院）；Trinity College Dublin（都柏林圣三一学院）；SciencesPo Paris（巴黎政治学院）；Dublin City University（都柏林城市大学）；Aristotle University of Thessaloniki（亚里士多德大学）；AIAAIC；Government of India（印度政府）；University of Maine School of Law（缅因大学法学院）；Algoma University（阿尔戈马大学）；New York University（纽约大学）；TU & WU Wien（维也纳技术大学与维也纳经济大学）
We propose a new method for generating explanations with AI and a tool to test its expressive power within a user interface. In order to bridge the gap between philosophy and human-computer interfaces, we show a new approach for the generation of interactive explanations based on a sophisticated pipeline of AI algorithms for structuring natural language documents into knowledge graphs, answering questions effectively and satisfactorily. With this work we aim to prove that the philosophical theory of explanations presented by Achinstein can be actually adapted for being implemented into a concr…
Which LIME should I trust? Concepts, Challenges, and Solutions
TU Clausthal（克劳斯塔尔工业大学）；University of Mannheim（曼海姆大学）；Ludwig-Maximilians-Universität München（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures
Griffith University（格里菲斯大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University of Bremen（不来梅大学）；Hanoi University of Science and Technology（河内科技大学）；The University of Queensland（昆士兰大学）
Integrating Feature Understandability into Feature Selection for Human-Centred Tabular Explanations
School of Computer Science and Information Technology, University College Cork（科克大学计算机科学与信息技术学院）；Centre for Research Training in Artificial Intelligence, University College Cork（科克大学人工智能研究培训中心）；Department of Methodology and Statistics, Tilburg University（蒂尔堡大学方法论与统计系）；Insight Centre for Data Analytics, University College Cork（科克大学Insight数据分析中心）
