More and more software practitioners are tackling towards industrial applications of artificial intelligence (AI) systems, especially those based on machine learning (ML). However, many of existing principles and approaches to traditional systems do not work effectively for the system behavior obtained by training not by logical design. In addition, unique kinds of requirements are emerging such as fairness and explainability. To provide clear guidance to understand and tackle these difficulties, we present an analysis on what quality concepts we should evaluate for AI systems. We base our discussion on ISO/IEC 25000 series, known as SQuaRE, and identify how it should be adapted for the unique nature of ML and $\textit{Ethics guidelines for trustworthy AI}$ from European Commission. We thus provide holistic insights for quality of AI systems by incorporating the ML nature and AI ethics to the traditional software quality concepts.
University of Oxford（牛津大学）；Cisco Research Centre（思科研究中心）；Cardiff University（卡迪夫大学）；University of Warwick（华威大学）
Multiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real time intelligence in edge computing. The paper pres…
An increasing number of artists use Ai in their creative practices (Creative-Ai) and their works have by now become visible at prominent art venues. The research community has, on the other hand, recognized that there are sustainability concerns of using Ai technologies related to, for instance, energy consumption and the increasing size and complexity of models. These two conflicting trajectories constitute the starting point of our research. Here, we discuss insights from our currently on-going fieldwork research and outline considerations for drawing various limitations in sustainability as…
Given AI's growing role in modeling and improving decision-making, how and when to present users with feedback is an urgent topic to address. We empirically examined the effect of feedback from false AI on moral decision-making about donor kidney allocation. We found some evidence that judgments about whether a patient should receive a kidney can be influenced by feedback about participants' own decision-making perceived to be given by AI, even if the feedback is entirely random. We also discovered different effects between assessments presented as being from human experts and assessments pres…
Dublin College University（都柏林学院大学）；Ulster University（阿尔斯特大学）；University of Science and Technology Beijing（北京科技大学）
Death by suicide is the seventh leading death cause worldwide. The recent advancement in Artificial Intelligence (AI), specifically AI applications in image and voice processing, has created a promising opportunity to revolutionize suicide risk assessment. Subsequently, we have witnessed fast-growing literature of research that applies AI to extract audiovisual non-verbal cues for mental illness assessment. However, the majority of the recent works focus on depression, despite the evident difference between depression symptoms and suicidal behavior and non-verbal cues. This paper reviews recen…
University of Wisconsin-Milwaukee（威斯康星大学密尔沃基分校）；AZH Wound Center（AZH伤口中心）
Efficient and effective assessment of acute and chronic wounds can help wound care teams in clinical practice to greatly improve wound diagnosis, optimize treatment plans, ease the workload and achieve health related quality of life to the patient population. While artificial intelligence (AI) has found wide applications in health-related sciences and technology, AI-based systems remain to be developed clinically and computationally for high-quality wound care. To this end, we have carried out a systematic review of intelligent image-based data analysis and system developments for wound assess…
The gigantic complexity and heterogeneity of today's advanced cyber-physical systems and systems of systems is multiplied by the use of avant-garde computing architectures to employ artificial intelligence based autonomy in the system. Here, the overall system's reliability comes along with requirements for fail-safe, fail-operational modes specific to the target applications of the autonomous system and adopted HW architectures. The paper makes an overview of reliability challenges for intelligence implementation in autonomous systems enabled by HW backbones such as neuromorphic architectures…
The University of Western Australia（西澳大利亚大学）；Armadale Mental Health Service（阿玛代尔心理健康服务中心）；Bethesda Clinic（贝塞斯达诊所）；Co-HIVE Mental Health（Co-HIVE心理健康中心）；Harry Perkins Institute of Medical Research（哈里·珀金斯医学研究所）；Fiona Stanley Hospital（菲奥娜·斯坦利医院）；University of Western Australia（西澳大利亚大学）
This study investigates the effectiveness of Explainable Artificial Intelligence (XAI) techniques in predicting suicide risks and identifying the dominant causes for such behaviours. Data augmentation techniques and ML models are utilized to predict the associated risk. Furthermore, SHapley Additive exPlanations (SHAP) and correlation analysis are used to rank the importance of variables in predictions. Experimental results indicate that Decision Tree (DT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models achieve the best results while DT has the best performance with an accur…
Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；National University of Singapore（新加坡国立大学）；University of Oxford（牛津大学）；Imperial College London（帝国理工学院）；University of Antwerp（安特卫普大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡全国眼科中心）；Singapore Health Services（新加坡保健服务）；Singapore General Hospital（新加坡中央医院）；Hangzhou Dianzi University（杭州电子科技大学）；Nanyang Technological University（南洋理工大学）；The Chinese University of Hong Kong（香港中文大学）；Scripps Research（斯克里普斯研究所）
Artificial intelligence for objective assessment of acrobatic movements: How to apply machine learning for identifying tumbling elements in cheer sports
New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography
New York University（纽约大学）；NYU Langone Health（NYU Langone健康中心）；NYU Tandon School of Engineering（NYU塔能工程学院）；Indiana University School of Medicine（印第安纳大学医学院）
In this paper we examine historical failures of artificial intelligence (AI) and propose a classification scheme for categorizing future failures. By doing so we hope that (a) the responses to future failures can be improved through applying a systematic classification that can be used to simplify the choice of response and (b) future failures can be reduced through augmenting development lifecycles with targeted risk assessments.
Computable Gap Assessment of Artificial Intelligence Governance in Children's Centres: Evidence-Mechanism-Governance-Indicator Modelling of UNICEF's Guidance on AI and Children 3.0 Based on the Graph-GAP Framework
Dhurakij Pundit University（杜斯特帕尼大学）；The University of Western Australia（西澳大学）；Royal Anthropological Institute（皇家人类学研究所）
University Medical Center Göttingen（哥廷根大学医学中心）；Cochrane Germany（德国考科蓝中心）；Universitätsklinikum Freiburg（弗莱堡大学附属医院）；Novartis Pharma AG（诺华制药股份公司）；University of Freiburg（弗莱堡大学）；University Bremen（不莱梅大学）；Federal Statistical Office of Germany（德国联邦统计局）；TU Dortmund University（多特蒙德工业大学）；Ulm University（乌尔姆大学）；Ruhr-Universität Bochum（波鸿鲁尔大学）；Universität Eichstätt-Ingolstadt（因戈尔施塔特- Eichstätt大学）；RWTH Aachen（亚琛工业大学）；Jacobs University Bremen（不莱梅雅各布斯大学）
The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion. Here we argue that statistics, as an interdisciplinary scientific field, plays a substantial role both for the theoretical and practical understanding of AI and for its future development. Statistics might even be considered a core element of AI. With its specialist knowledge of data evaluation, starting with the precise formulation of the research question and passing through a study design stage on to analysis and interpretation of the results, …
Artificial Intelligence (AI) technology is based on theory and development of computer systems able to perform tasks that normally require human intelligence. In this context, deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior. Application of these methods to medical imaging can assist pathologists in the detection of cancer subtype, gene mutations and/or metastases for applying appropriate therapies. The purpose of this study is to show the emerging application of AI in med…
Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of sup…
MOSCOW HEALTH CARE DEPARTMENT RESEARCH AND PRACTICAL CLINICAL CENTER FOR DIAGNOSTICS AND TELEMEDICINE TECHNOLOGIES（莫斯科卫生部诊断与远程医疗技术研究与实践临床中心）；MOSCOW HEALTH CARE DEPARTMENT BEST PRACTICES IN MEDICAL IMAGING（莫斯科卫生部医学影像最佳实践中心）
Aim: provide a methodological framework for the process of clinical tests, clinical acceptance, and scientific assessment of algorithms and software based on the artificial intelligence (AI) technologies. Clinical tests are considered as a preparation stage for the software registration as a medical product. The authors propose approaches to evaluate accuracy and efficiency of the AI algorithms for radiology.
The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges
University of Alberta（阿尔伯塔大学）；Federation of American Scientists（美国科学家联合会）；Educational Testing Service（教育考试服务中心）；University of Maryland（马里兰大学）；University of Iowa（爱荷华大学）；Cambium Assessment（坎比姆评估公司）；Washington State University（华盛顿州立大学）；Acuity Insights（敏锐洞察公司）；PSI Services（PSI服务公司）；University of Delaware（特拉华大学）；Concordia University of Edmonton（埃德蒙顿康考迪亚大学）
