We are used to the availability of big data generated in nearly all fields of science as a consequence of technological progress. However, the analysis of such data possess vast challenges. One of these relates to the explainability of artificial intelligence (AI) or machine learning methods. Currently, many of such methods are non-transparent with respect to their working mechanism and for this reason are called black box models, most notably deep learning methods. However, it has been realized that this constitutes severe problems for a number of fields including the health sciences and criminal justice and arguments have been brought forward in favor of an explainable AI. In this paper, we do not assume the usual perspective presenting explainable AI as it should be, but rather we provide a discussion what explainable AI can be. The difference is that we do not present wishful thinking but reality grounded properties in relation to a scientific theory beyond physics.
Meta Reality Labs（Meta现实实验室）；Purdue（普渡大学）；Aarhus Univ（奥胡斯大学）；Dartmouth（达特茅斯学院）；Cambridge（剑桥大学）
Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequently interact with intelligent services. However, it is unclear how to design effective XAI experiences for AR. We propose XAIR, a design framework that addresses "when", "what", and "how" to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing …
A plethora of recent research has proposed several automated methods based on machine learning (ML) and deep learning (DL) to detect cybersickness in Virtual reality (VR). However, these detection methods are perceived as computationally intensive and black-box methods. Thus, those techniques are neither trustworthy nor practical for deploying on standalone VR head-mounted displays (HMDs). This work presents an explainable artificial intelligence (XAI)-based framework VR-LENS for developing cybersickness detection ML models, explaining them, reducing their size, and deploying them in a Qualcom…
Rutgers University, School of Arts and Sciences Newark（罗格斯大学纽瓦克文理学院）
Limited expert time is a key bottleneck in medical imaging. Due to advances in image classification, AI can now serve as decision-support for medical experts, with the potential for great gains in radiologist productivity and, by extension, public health. However, these gains are contingent on building and maintaining experts' trust in the AI agents. Explainable AI may build such trust by helping medical experts to understand the AI decision processes behind diagnostic judgements. Here we introduce and evaluate explanations based on Bayesian Teaching, a formal account of explanation rooted in …
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 …
Entropy-Centric Explainable AI for Remote Sensing Image Segmentation
Faculty of Engineering, Lebanese University（黎巴嫩大学工程学院）；University of Paris-Est Créteil (UPEC)（巴黎东部克雷泰伊大学）；EFREI（EFREI学院）；National Center for Remote Sensing - CNRS（国家遥感中心 - 法国国家科学研究中心）
University of Missouri（密苏里大学）；Kennesaw State University（肯尼索州立大学）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）
Cybersickness is a common ailment associated with virtual reality (VR) user experiences. Several automated methods exist based on machine learning (ML) and deep learning (DL) to detect cybersickness. However, most of these cybersickness detection methods are perceived as computationally intensive and black-box methods. Thus, those techniques are neither trustworthy nor practical for deploying on standalone energy-constrained VR head-mounted devices (HMDs). In this work, we present an explainable artificial intelligence (XAI)-based framework, LiteVR, for cybersickness detection, explaining the …
Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions
Hamad bin Khalifa University（哈马德·本·哈利法大学）；Qatar Computing Research Institute（卡塔尔计算研究所）；Munster Technological University（芒斯特理工大学）；Toronto Metropolitan University（多伦多都会大学）
From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?
School of Computer Science and Electronic Engineering, University of Essex（埃塞克斯大学计算机科学与电子工程学院）；Harvard Medical School（哈佛医学院）；Boston Children’s Hospital（波士顿儿童医院）；University of Southampton（南安普顿大学）；Athena Research Centre（雅典娜研究中心）
Explanation mechanisms from the field of Counterfactual Thinking are a widely-used paradigm for Explainable Artificial Intelligence (XAI), as they follow a natural way of reasoning that humans are familiar with. However, all common approaches from this field are based on communicating information about features or characteristics that are especially important for an AI's decision. We argue that in order to fully understand a decision, not only knowledge about relevant features is needed, but that the awareness of irrelevant information also highly contributes to the creation of a user's mental…
The Belief-Desire-Intention Ontology for modelling mental reality and agency
CNR - Institute of Cognitive Sciences and Technologies（CNR认知科学与技术研究所）；CNR - Institute for Research on Population and Social Policies（CNR人口与社会政策研究所）；CNR - Research Institute on Sustainable Economic Growth（CNR可持续经济增长研究所）；University of Bologna - Department of Philosophy（博洛尼亚大学哲学系）
Qlector d.o.o.（Qlector有限公司）；Ubitech Ltd（Ubitech有限公司）；University of West Attica（西阿提卡大学）；R2M Solution Srl（R2M解决方案股份公司）；University of Cagliari（卡利亚里大学）；University of Piraeus（比雷埃夫斯大学）；National Technical University of Athens（雅典国立技术大学）；INTRASOFT International（INTRASOFT国际公司）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
Human-centricity is the core value behind the evolution of manufacturing towards Industry 5.0. Nevertheless, there is a lack of architecture that considers safety, trustworthiness, and human-centricity at its core. Therefore, we propose an architecture that integrates Artificial Intelligence (Active Learning, Forecasting, Explainable Artificial Intelligence), simulated reality, decision-making, and users' feedback, focusing on synergies between humans and machines. Furthermore, we align the proposed architecture with the Big Data Value Association Reference Architecture Model. Finally, we vali…
Jožef Stefan International Postgraduate School（约热夫·斯特凡国际研究生院）；Jožef Stefan Institute（约热夫·斯特凡研究所）；Qlector d.o.o.（Qlector 有限公司）；University of West Attica（西阿提卡大学）；Ubitech Ltd（Ubitech 有限公司）；R2M Solution Srl（R2M Solution 有限公司）；University of Cagliari（卡利亚里大学）
There is a lack of a single architecture specification that addresses the needs of trusted and secure Artificial Intelligence systems with humans in the loop, such as human-centered manufacturing systems at the core of the evolution towards Industry 5.0. To realize this, we propose an architecture that integrates forecasts, Explainable Artificial Intelligence, supports collecting users' feedback, and uses Active Learning and Simulated Reality to enhance forecasts and provide decision-making recommendations. The architecture security is addressed as a general concern. We align the proposed arch…
Optimizing economic and public policy is critical to address socioeconomic issues and trade-offs, e.g., improving equality, productivity, or wellness, and poses a complex mechanism design problem. A policy designer needs to consider multiple objectives, policy levers, and behavioral responses from strategic actors who optimize for their individual objectives. Moreover, real-world policies should be explainable and robust to simulation-to-reality gaps, e.g., due to calibration issues. Existing approaches are often limited to a narrow set of policy levers or objectives that are hard to measure, …
