Abstract—Building trustworthy artificial intelligence (AI) solu tions, whether in academia or industry, must take into considera tion a number of dimensions including legal, social, ethical, public opinion, and environmental aspects. A plethora of guidelines, prin ciples, and toolkits have been published globally, but have seen limited grassroots implementation, especially among small- and medium-sizedenterprises(SMEs),mainlyduetothelackofknowl edge, skills, and resources. In this article, we report on qualitative SMEconsultationsovertwoeventstoestablishtheirunderstanding of both data and AI ethical principles and to identify the key barriers SMEs face in their adoption of ethical AI approaches. Wethen use independent experts to review and code 77 published toolkits designed to build and support ethical and responsible AI practices, based on 33 evaluation criteria. The toolkits were evalu ated considering their scope to address the identified SMEbarriers to adoption, human-centric AI principles, AI life cycle stages, and key themes around responsible AI and practical usability. Toolkits wererankedonthebasisofcriteriacoverageandexpertintercoder agreement. Results show that there is not a one-size-fits-all toolkit that addresses all criteria suitable for SMEs. Our findings show few exemplars of practical application, little guidance on how to use/apply the toolkits, and very low uptake by SMEs. Our analysis provides a mechanism for SMEs to select their own toolkits based on their current capacity, resources, and ethical awareness levels focusing initially at the conceptualization stage of the AI life cycle and then extending throughout.

Abstract—Artificial intelligence (AI) has profoundly changed and will continue to change our lives. AI is being applied in more and more fields and scenarios such as autonomous driving, med ical care, media, finance, industrial robots, and internet services. The widespread application of AI and its deep integration with the economy and society have improved efficiency and produced benefits. At the same time, it will inevitably impact the existing social order and raise ethical concerns. Ethical issues, such as privacyleakage,discrimination,unemployment,andsecurityrisks, brought about by AI systems have caused great trouble to people. Therefore, AI ethics, which is a field related to the study of ethical issues in AI, has become not only an important research topic in academia, but also an important topic of common concern for individuals, organizations, countries, and society. This article will give a comprehensive overview of this field by summarizing and analyzingtheethicalrisksandissuesraisedbyAI,ethicalguidelines and principles issued by different organizations, approaches for addressing ethical issues in AI, and methods for evaluating the ethics of AI. Additionally, challenges in implementing ethics in AI and some future perspectives are pointed out. We hope our work will provide a systematic and comprehensive overview of AI ethics for researchers and practitioners in this field, especially the beginners of this research discipline.

INORDERto ensure the safe operation of automation sys tems, maintenance and safety have been active issues for automation systems. By discovering quantitative or qualitative knowledge hidden in measurements of automation systems, artificial intelligence (AI) methods can carry on these tasks in a human-like way. Therefore, issues, such as AI-based fault diag nosis (FD),fault-tolerant control, together withfaultprognostics receive enhanced attention in both engineering and research domains over the past decades. The primary objective of this Special Issue, titled “AI Methods for Maintenance and Safety of Automation Systems,” of the IEEE TRANSACTIONS ON ARTIFI CIAL INTELLIGENCE is to provide the related latest achievements made by researchers and practitioners on the one hand, and to identify critical issues and challenges for future investigation on the other hand

INTHISeditorial,Iposethequestion ofhowwecanincrease the number of papers we publish on interdisciplinary arti f icial intelligence (AI) in the IEEE Transactions on AI (IEEE TAI). Some of our readers may disagree with the premise of the question. After all, IEEE TAI has published many application papers with multidisciplinary teams, and IEEE TAI hosted a theme on COVID-19 that saw papers bringing together medical practitioners and computer, data and AI scientists, information systems researchers, software engineers, and more. In addition, IEEETAIhaspublished papers on AI in healthcare, agriculture, drug discovery, cyber security, physics, and more, involving collaborations among diverse team members. However, I label these papers “multidisciplinary” and not “interdisciplinary.”

Abstract—Theblack-boxnatureofmachinelearningmodelshin dersthedeploymentofsomehigh-accuracymedicaldiagnosisalgo rithms.Itisriskytoputone’slifeinthehandsofmodelsthatmedical researchers do not fully understand or trust. However, through modelinterpretation,black-boxmodelscanpromptlyrevealsignif icant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic. This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th January 2020 and 5th March 2020, in Zhuhai, China, to identify biomarkers indicative of infection severity prediction. Through the interpretation of four machine learning models, decision tree, random forests, gradient boosted trees, and neural networks using permutation feature im portance, partial dependence plot, individual conditional expecta tion, accumulated local effects, local interpretable model-agnostic explanations, and Shapley additive explanation, we identify an increase in N-terminal pro-brain natriuretic peptide, C-reaction protein, and lactic dehydrogenase, a decrease in lymphocyte is associated with severe infection and an increased risk of death, which is consistent with recent medical research on COVID-19 and other research using dedicated models. We further validate our methods on a large open dataset with 5644 confirmed patients from the Hospital Israelita Albert Einstein, at São Paulo, Brazil