In this paper, we provide a comprehensive outline of the different threads of work in Explainable AI Planning (XAIP) that has emerged as a focus area in the last couple of years and contrast that with earlier efforts in the field in terms of techniques, target users, and delivery mechanisms. We hope that the survey will provide guidance to new researchers in automated planning towards the role of explanations in the effective design of human-in-the-loop systems, as well as provide the established researcher with some perspective on the evolution of the exciting world of explainable planning.
Explainable AI is an important area of research within which Explainable Planning is an emerging topic. In this paper, we argue that Explainable Planning can be designed as a service -- that is, as a wrapper around an existing planning system that utilises the existing planner to assist in answering contrastive questions. We introduce a prototype framework to facilitate this, along with some examples of how a planner can be used to address certain types of contrastive questions. We discuss the main advantages and limitations of such an approach and we identify open questions for Explainable Pl…
In recent years, the field of explainable AI (XAI) has produced a vast collection of algorithms, providing a useful toolbox for researchers and practitioners to build XAI applications. With the rich application opportunities, explainability is believed to have moved beyond a demand by data scientists or researchers to comprehend the models they develop, to an essential requirement for people to trust and adopt AI deployed in numerous domains. However, explainability is an inherently human-centric property and the field is starting to embrace human-centered approaches. Human-computer interactio…
University of Washington（华盛顿大学）；Arthur AI；BITS Pilani, Goa Campus（贝拉理工学院果阿校区）；University of Maryland（马里兰大学）
As machine learning (ML) systems take a more prominent and central role in contributing to life-impacting decisions, ensuring their trustworthiness and accountability is of utmost importance. Explanations sit at the core of these desirable attributes of a ML system. The emerging field is frequently called ``Explainable AI (XAI)'' or ``Explainable ML.'' The goal of explainable ML is to intuitively explain the predictions of a ML system, while adhering to the needs to various stakeholders. Many explanation techniques were developed with contributions from both academia and industry. However, the…
The Defense Advanced Research Projects Agency (DARPA) recently launched the Explainable Artificial Intelligence (XAI) program that aims to create a suite of new AI techniques that enable end users to understand, appropriately trust, and effectively manage the emerging generation of AI systems. In this paper, inspired by DARPA's XAI program, we propose a new paradigm in security research: Explainable Security (XSec). We discuss the ``Six Ws'' of XSec (Who? What? Where? When? Why? and How?) and argue that XSec has unique and complex characteristics: XSec involves several different stakeholders (…
Centre for Forensic Science, University of Technology Sydney（悉尼科技大学法医科学中心）
Explainable Artificial Intelligence (XAI) is an emerging area of research in the field of Artificial Intelligence (AI). XAI can explain how AI obtained a particular solution (e.g., classification or object detection) and can also answer other "wh" questions. This explainability is not possible in traditional AI. Explainability is essential for critical applications, such as defense, health care, law and order, and autonomous driving vehicles, etc, where the know-how is required for trust and transparency. A number of XAI techniques so far have been purposed for such applications. This paper pr…
Exploring Energy Landscapes for Minimal Counterfactual Explanations: Applications in Cybersecurity and Beyond
Netcompany-Intrasoft S.A.（Netcompany-Intrasoft公司）；Hellenic Mediterranean University（希腊地中海大学）；Harokopio University of Athens（雅典哈罗科皮奥大学）；University of Western Macedonia（西马其顿大学）
Artificial Intelligence (AI) has become an integral part of domains such as security, finance, healthcare, medicine, and criminal justice. Explaining the decisions of AI systems in human terms is a key challenge--due to the high complexity of the model, as well as the potential implications on human interests, rights, and lives . While Explainable AI is an emerging field of research, there is no consensus on the definition, quantification, and formalization of explainability. In fact, the quantification of explainability is an open challenge. In our previous work, we incorporated domain knowle…
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）
Forschungszentrum Jülich（于利希研究中心）；University of Cologne（科隆大学）；Queen Mary University of London（伦敦玛丽女王大学）；Norwegian University of Life Sciences（挪威生命科学大学）
The transition to a renewable energy system poses challenges for power grid operation and stability. Secondary control is key in restoring the power system to its reference following a disturbance. Underestimating the necessary control capacity may require emergency measures, such as load shedding. Hence, a solid understanding of the emerging risks and the driving factors of control is needed. In this contribution, we establish an explainable machine learning model for the activation of secondary control power in Germany. Training gradient boosted trees, we obtain an accurate description of co…
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…
Artificial Intelligence for CRISPR Guide RNA Design: Explainable Models and Off-Target Safety
Department of Computer Engineering, Ma.C., Islamic Azad University（计算机工程系，伊斯兰阿兹德大学）；Department of Biological Sciences and Technologies, Faculty of Basic Sciences, Islamic Azad University（基础科学学院生物科学与技术系，伊斯兰阿兹德大学）
Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems
Queen’s University（女王大学）；Newcastle University（纽卡斯尔大学）；University of Stirling（斯特灵大学）；University of Parma（帕尔马大学）；Robert Gordon University（罗伯特戈登大学）；University of Trento（特伦托大学）；Universiteit Leiden（莱顿大学）；University of Exeter（埃克塞特大学）
Sparse, Efficient and Explainable Data Attribution with DualXDA
Department of Artificial Intelligence Fraunhofer Heinrich Hertz Institute（人工智能系 汉诺威弗劳恩霍夫研究所）；Department of Electrical Engineering and Computer Science Technische Universität Berlin（电气工程与计算机科学系 柏林技术大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Centre of eXplainable Artificial Intelligence Technological University Dublin（可解释人工智能中心 立陶宛技术大学）
National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Royal Brompton Hospital（皇家布朗普顿医院）；Imperial Institute of Advanced Technology（帝国理工高等研究院）；Hangzhou Ocean’s Smart Boya Co., Ltd（杭州海洋智能博雅有限公司）；University of California, San Diego（加州大学圣地亚哥分校）；Shenzhen Second People’s Hospital（深圳市第二人民医院）
Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made. This research field inspects the measures and models involved in decision-making and seeks solutions to explain them explicitly. Many of the machine learning algorithms can not manifest how and why a decision has been cast. This is particularly true of the most popular deep neural network approaches currently in use. Consequently, our confidence in AI systems can be hindered by the lack of explainability in these black-box models. The XAI bec…
There has been significant interest of late in generating behavior of agents that is interpretable to the human (observer) in the loop. However, the work in this area has typically lacked coherence on the topic, with proposed solutions for "explicable", "legible", "predictable" and "transparent" planning with overlapping, and sometimes conflicting, semantics all aimed at some notion of understanding what intentions the observer will ascribe to an agent by observing its behavior. This is also true for the recent works on "security" and "privacy" of plans which are also trying to answer the same…
Evolving Safety Landscape of Multi-modal Large Language Models: A Survey of Emerging Threats and Safeguards
University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；NVIDIA（英伟达公司）；Penn State University（宾夕法尼亚州立大学）；Columbia University（哥伦比亚大学）；University of Missouri-Kansas City（密苏里大学堪萨斯分校）；Florida State University（佛罗里达州立大学）；Auburn University（奥本大学）
Curtin University（科廷大学）；Deakin University（迪肯大学）；Technical University of Berlin（柏林工业大学）；Huazhong University of Science and Technology（华中科技大学）
Explainable artificial intelligence is an emerging and evolving concept. Its impact on construction, though yet to be realised, will be profound in the foreseeable future. Still, XAI has received limited attention in construction. As a result, no evaluation frameworks have been propagated to enable construction organisations to understand the what, why, how, and when of XAI. Our paper aims to fill this void by developing a content, context, process, and outcome evaluation framework that can be used to justify the adoption and effective management of XAI. After introducing and describing this n…
In the last five years, private companies, research institutions as well as public sector organisations have issued principles and guidelines for ethical AI, yet there is debate about both what constitutes "ethical AI" and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analyzed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, n…
Building Trust in Conversational AI: A Comprehensive Review and Solution Architecture for Explainable, Privacy-Aware Systems using LLMs and Knowledge Graph
CeADAR - Ireland’s Centre for Applied AI, University College Dublin（爱尔兰应用人工智能中心CeADAR、都柏林大学学院）
The emerging meta- and multi-verse landscape is yet another step towards the more prevalent use of already ubiquitous online markets. In such markets, recommender systems play critical roles by offering items of interest to the users, thereby narrowing down a vast search space that comprises hundreds of thousands of products. Recommender systems are usually designed to learn common user behaviors and rely on them for inference. This approach, while effective, is oblivious to subtle idiosyncrasies that differentiate humans from each other. Focusing on this observation, we propose an architectur…
Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videos
Department of Electrical and Computer Engineering, Northeastern University（电气与计算机工程系，东北大学）；School of Data Science, University of Virginia（数据科学学院，弗吉尼亚大学）；Department of Electrical and Computer Engineering and Khoury College of Computer Science, Northeastern University（电气与计算机工程系和Khoury计算机科学学院，东北大学）
