We describe an extension of the Fanoos XAI system [Bayani et al 2022] which enables the system to learn the appropriate action to take in order to satisfy a user's request for description to be made more or less abstract. Specifically, descriptions of systems under analysis are stored in states, and in order to make a description more or less abstract, Fanoos selects an operator from a large library to apply to the state and generate a new description. Prior work on Fanoos predominately used hand-written methods for operator-selection; this current work allows Fanoos to leverage experience to learn the best operator to apply in a particular situation, balancing exploration and exploitation, leveraging expert insights when available, and utilizing similarity between the current state and past states. Additionally, in order to bootstrap the learning process (i.e., like in curriculum learning), we describe a simulated user which we implemented; this simulation allows Fanoos to gain general insights that enable reasonable courses of action, insights which later can be refined by experience with real users, as opposed to interacting with humans completely from scratch. Code implementing the methods described in the paper can be found at https://github/DBay-ani/Operator_Selection_Learning_Extensions_For_Fanoos.
The XAI literature is decentralized, both in terminology and in publication venues, but recent years saw the community converge around keywords that make it possible to more reliably discover papers automatically. We use keyword search using the SemanticScholar API and manual curation to collect a well-formatted and reasonably comprehensive set of 5199 XAI papers, available at https://github.com/alonjacovi/XAI-Scholar . We use this collection to clarify and visualize trends about the size and scope of the literature, citation trends, cross-field trends, and collaboration trends. Overall, XAI i…
Know-Center GmbH（Know-Center有限公司）；University of Technology Graz（格拉茨技术大学）
Deep learning models have recently demonstrated remarkable results in a variety of tasks, which is why they are being increasingly applied in high-stake domains, such as industry, medicine, and finance. Considering that automatic predictions in these domains might have a substantial impact on the well-being of a person, as well as considerable financial and legal consequences to an individual or a company, all actions and decisions that result from applying these models have to be accountable. Given that a substantial amount of data that is collected in high-stake domains are in the form of ti…
Should XAI Nudge Human Decisions with Explanation Biasing?
Tokyo Metropolitan University（东京都立大学）；National Institute of Informatics（信息学研究所）；The Graduate University for Advanced Studies (SOKENDAI)（高级研究大学院大学（SOKENDAI））
LUT University（拉彭兰塔-拉赫蒂工业大学）；Norwegian University of Science and Technology（挪威科技大学）；SINTEF Digital（挪威科技工业研究院数字研究所）
The final search query for the Systematic Literature Review (SLR) was conducted on 15th July 2022. Initially, we extracted 1707 journal and conference articles from the Scopus and Web of Science databases. Inclusion and exclusion criteria were then applied, and 58 articles were selected for the SLR. The findings show four dimensions that shape the AI explanation, which are format (explanation representation format), completeness (explanation should contain all required information, including the supplementary information), accuracy (information regarding the accuracy of the explanation), and c…
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）
University of Konstanz（康斯坦茨大学）；Offenburg University of Applied Sciences（奥芬堡应用科学大学）
Decision explanations of machine learning black-box models are often generated by applying Explainable AI (XAI) techniques. However, many proposed XAI methods produce unverified outputs. Evaluation and verification are usually achieved with a visual interpretation by humans on individual images or text. In this preregistration, we propose an empirical study and benchmark framework to apply attribution methods for neural networks developed for images and text data on time series. We present a methodology to automatically evaluate and rank attribution techniques on time series using perturbation…
VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis
Scientific Computing and Imaging Institute, University of Utah, USA（美国犹他大学科学计算与成像研究所）；University of California, Davis, USA（美国加州大学戴维斯分校）；Bosch Research North America and Bosch Center for Artificial Intelligence (BCAI), USA（美国博世北美研究部及博世人工智能中心）；Robert Bosch GmbH, Germany（德国博世集团）；Splunk Technology, USA（美国Splunk技术公司）
XAI-SOH-FL: Enhancing SOH-FL with Adaptive Aggregation and Explainable AI for Intrusion Detection in Heterogeneous IoT
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST)（电气工程与计算机科学学院（SEECS），国家 sciences and Technology（NUST））
On the Role of Domain Experts in Creating Effective Tutoring Systems
Department of Computer Science, Colorado State University, USA（科罗拉多州立大学计算机科学系）；Colorado State University Extension, USA（科罗拉多州立大学扩展服务）；School of Education, Colorado State University, USA（科罗拉多州立大学教育学院）
Explanova: Automatically Discover Data Insights in N \times M Table via XAI Combined LLM Workflow
Institute for Clarity in Documentation（清晰文档研究所）；Inria Paris-Rocquencourt（巴黎-罗克Quantin 国家信息与自动化研究所）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕尔默研究实验室）
ISTI-CNR（意大利国家研究委员会信息科学与技术研究所）；University of Pisa（比萨大学）；Criteo AI Lab（Criteo人工智能实验室）；Scuola Normale Superiore of Pisa（比萨高等师范学校）
A key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making systems. Research in eXplainable Artificial Intelligence (XAI) is trying to solve this issue. However, often XAI approaches are only tested on generalist classifier and do not represent realistic problems such as those of medical diagnosis. In this paper, we analyze a case study on skin lesion images where we customize an existing XAI approach for explaining a deep learning model able to recognize different types of skin lesions. The explanation is formed by…
LAG-XAI: A Lie-Inspired Affine Geometric Framework for Interpretable Paraphrasing in Transformer Latent Spaces
Khmelnytskyi National University（赫梅利尼茨基国立大学）；Taras Shevchenko National University of Kyiv（基辅塔拉斯·舍甫琴科国立大学）；V.M. Glushkov Institute of Cybernetics of NAS of Ukraine（乌克兰国家科学院V.M.格卢什科夫控制论研究所）
Machine learning (ML) systems across many application areas are increasingly demonstrating performance that is beyond that of humans. In response to the proliferation of such models, the field of Explainable AI (XAI) has sought to develop techniques that enhance the transparency and interpretability of machine learning methods. In this work, we consider a question not previously explored within the XAI and ML communities: Given a computational system whose performance exceeds that of its human user, can explainable AI capabilities be leveraged to improve the performance of the human? We study …
Universidad Autonoma de Madrid（马德里自治大学）；Laboratoire des Sciences du Numérique de Nantes（南特数字科学实验室）；National Institute of Informatics（国立信息学研究所）
Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods can become crucial. Inductive Logic Programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the process of data. Learning from Interpretation Transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given black-box system (under certain co…
Towards Biomarker Discovery for Early Cerebral Palsy Detection: Evaluating Explanations Through Kinematic Perturbations
Norwegian University of Science and Technology（挪威科技大学）；Faculty of Medicine and Health Sciences（医学与健康科学学院）；Faculty of Information Technology and Electrical Engineering（信息技术与电气工程学院）；Faculty of Medicine（医学院）
ENSTA, Institut 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）；Segula Technologies（赛科集团）；Sorbonne Université（索邦大学）；Free University of Bozen-Bolzano（波尔扎诺自由大学）；Tinubu Square（Tinubu Square公司）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）；City University London（伦敦城市大学）；Medical University Graz（格拉茨医科大学）；INESC-ID（葡萄牙信息与通信技术研究院）；Instituto Superior Técnico（里斯本高等理工学院）；Institut Supérieur d’Électronique de Paris (ISEP)（巴黎高等电子学院）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）
Last years have been characterized by an upsurge of opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although they have great generalization and prediction skills, their functioning does not allow obtaining detailed explanations of their behaviour. As opaque machine learning models are increasingly being employed to make important predictions in critical environments, the danger is to create and use decisions that are not justifiable or legitimate. Therefore, there is a general agreement on the importance of endowing machine learning models with explainability. E…
Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable AI and Federated Learning
Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School（布伦瑞克工业大学与汉诺威医学院彼得·L·赖歇茨医学信息学研究所）；Hannover Medical School（汉诺威医学院）
Counterfactual explanations (CFEs) highlight what changes to a model's input would have changed its prediction in a particular way. CFEs have gained considerable traction as a psychologically grounded solution for explainable artificial intelligence (XAI). Recent innovations introduce the notion of computational plausibility for automatically generated CFEs, enhancing their robustness by exclusively creating plausible explanations. However, practical benefits of such a constraint on user experience and behavior is yet unclear. In this study, we evaluate objective and subjective usability of co…
Today, AI technology is showing its strengths in almost every industry and walks of life. From text generation, text summarization, chatbots, NLP is being used widely. One such paradigm is automatic code generation. An AI could be generating anything; hence the output space is unconstrained. A self-driving car is driven for 100 million miles to validate its safety, but tests cannot be written to monitor and cover an unconstrained space. One of the solutions to validate AI-generated content is to constrain the problem and convert it from abstract to realistic, and this can be accomplished by ei…
Easydiagnos: a framework for accurate feature selection for automatic diagnosis in smart healthcare
Dr. B. C. Roy Engineering College（B.C.罗伊工程学院）；Bengal College of Engg. and Tech.（孟加拉工程技术学院）；National Institute of Technology（印度国家理工学院）；University of North Texas（北得克萨斯大学）
本文提出 Easydiagnos 框架，通过融合遗传算法、可解释 AI 和排列组合技术的 AFE 算法优化医疗特征选择，并在多个数据集上验证其可提升临床决策的准确性与可解释性。
