Specific Artificial Intelligence Research Articles

Tracing the legitimacy of Artificial Intelligence: A longitudinal analysis of media discourse

Artificial Intelligence (AI) is one of the most relevant technologies of our time. During the last decade, AI has made major technological breakthroughs most recently in the space of generative AI. This development is enabled by an increase in computing power, a decrease in its price, and the emergence of ubiquitous computing, resulting in vast amounts of storable and processable data. However, the diffusion of AI depends on its legitimacy in society, whereby legitimacy is understood as the congruence between organizational activities and their cultural environment. This study aims at understanding the process whereby AI is being (de)legitimated across key industries and over time. To capture and trace the process of legitimation, we rely on media coverage as a form of societal discourse reflecting the legitimation process of AI. We find that the legitimation process gathers momentum in the mid-2010s and the legitimacy of AI increases over time. Furthermore, we identify four types of legitimacy discourse, which integrate sentiment, specific media frames, and appeals of persuasion. Uncovering the four types of legitimacy discourse, we aim at supporting organizations seeking to understand the legitimacy of specific AI applications and how these legitimacy judgments can shift.

Smart digital platforms for carbon neutral management and services: Business models based on ITU standards for green digital transformation

This brief research report focuses on the effects of artificial intelligence (AI) on the environment, by analyzing the latest documents issued by major standard organizations such as the International Telecommunication Union (ITU) and the Internet Society of China (ISC). By outlining the latest developments into a platform canvas for carbon neutrality management and services, this report identifies the potential of “AI of the environment” (i.e., the material composition and environmental impact of AI itself) and “AI for the environment” (i.e., the purposeful use of smart applications to benefit the environment). The role of AI is contextualized in the digital platform design for the provision of services on carbon emission data, which serves as the material foundation for smart services facing both the producers and the consumers of such information. Contributing to the design of business models that enable open innovations, this report discusses the emission impact reduction mechanisms that can optimize, substitute, induce, manage, and facilitate processes and services, indicating the potential of AI-enabled smart services such as forecasting, planning, and recommendation systems. Despite the limited disciplinary considerations and detailed discussions on specific AI technologies, this report provides a simple, practical, and flexible technology roadmap that can be used as a guide for researchers and practitioners to refine their operations and designs and to follow best practices. This report succinctly visualizes key elements of digital platforms of/for GHG emission reduction and their enabling mechanisms, serving as an AI technology roadmap for future research and innovation in the field.

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Artificial intelligence (AI) has emerged as the most widely applicable field across varied industries. Being an evolving technology, it may be quite useful in sensitive areas such as cyber security where there is a dire need for implementation of AI technologies, such as expert systems, neural networks, intelligent agents, and artificial immune systems. The primary reason for AI fitment to cyber security area is its ability to detect anomalies proactively and predictively in the network, thereby working towards securing the network before the damage related to loss of data and/or reputation is done. There are different types of AI technologies as mentioned above that could be applied in cyber security in its varied forms. In this paper, the emphasis is on specific AI technologies that can bring unique benefits to the cyber security field with its unique applicability to different scenarios. The outcome of this study shows that AI technologies such as expert systems, neural networks, intelligent agents, and artificial immune systems are transforming the landscape for managing cyber threats.

Applied Artificial Intelligence in Manufacturing and Industrial Production Systems: PEST Considerations for Engineering Managers

Presently, artificial intelligence (AI) is playing a leading role in our contemporary world via numerous applications. Despite its many advantages, analytical frameworks highlighting the implications of AI applications are still evolving. Particularly, in manufacturing and industrial production where novel technologies are continuously being harnessed. Consequently, AI and the implications of its applications have relatively remained a grey area for many engineering managers who are key players in the gravitation of manufacturing and industrial production towards the fourth industrial revolution and more recently, the fifth industrial revolution, generally termed as Industry 4.0 (I4.0) and Industry 5.0 (I5.0), respectively. In this study, the implications of AI applications in the general context of manufacturing and industrial production, are presented to provide insight for engineering managers. These implications are discussed via PEST ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u> olitical, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</u> conomic, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> ocial, and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</u> echnological) considerations of the broad implications of the adoption of AI techniques in manufacturing and industrial production systems. A new engineering management model has not been proposed in this paper. Rather, a discussion aimed at serving as a tool for the appraisal of the implications of the general applications of AI by engineering managers, who may not be AI specialists or data science experts is presented.

Understanding the Worldwide Paths Towards the Creation of True Intelligence for Machines

Nowadays, we remark that breakthroughs in the field of Artificial Intelligence (AI) suggesting its similarity with human beings, tremendous diversity of subfields and terminologies implied in the AI discipline, huge diversity of AI techniques, mistakes of AI and hype could lead to confusion about a clear understanding of the field (due to multiplicity of elements, brilliant successes, and senseless failures at the same time). In some cases, misunderstanding about AI led to hype, firing, and rude criticism even among many senior experts of the AI domain. Therefore, we detected the need for a short and very comprehensive overview of the whole and very vast AI field (as a good and useful reference) for providing fast insights leading to a better contextual understanding. And all of this by putting all aspects of AI together in few pages, based on practical and realistic (empirical) studies. Indeed, as only long training paths based on several outstanding books can fully cover all aspects of the AI discipline in several years, a short AI approach with shallow technical aspects would be suitable for everybody no matter their fields of activity, and so would contribute to avoiding misunderstandings about AI. Subsequently, in the situation where the digitization and involvement of AI appears on a global level in all fields of activity, we let the very hard complex technical aspects (requiring at least sophomore level of mathematics) to (we) AI specialists only. In this paper, we proposed “Understanding the Worldwide Paths towards the Creation of True Intelligence for Machines” so that everyone (starting with newbies) is able, via clear insights, to make a difference rapidly. As our modest contribution to scientific literature, we unambiguously showed via carefully designed illustrations and discussions, how the AI realm is held by well-known Theories of Intelligence and related AI Concepts that perfectly match the current technological advances in the AI field, and also future objectives. And, of course, we provided a clear insight into Ethical concerns about Artificial Intelligence.

Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade

Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the practice of pathology. However, clinical integration remains challenging, with no AI algorithms to date in routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience. Perspectives were solicited using the Delphi method from 24 subject matter experts between December 2020 and February 2021 regarding the anticipated role of AI in pathology by the year 2030. The study consisted of three consecutive rounds: 1) an open-ended, free response questionnaire generating a list of survey items; 2) a Likert-scale survey scored by experts and analysed for consensus; and 3) a repeat survey of items not reaching consensus to obtain further expert consensus. Consensus opinions were reached on 141 of 180 survey items (78.3%). Experts agreed that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030. High consensus was reached on 100 items across nine categories encompassing the impact of AI on (1) pathology key performance indicators (KPIs) and (2) the pathology workforce and specific tasks performed by (3) pathologists and (4) AP lab technicians, as well as (5) specific AI applications and their likelihood of routine use by 2030, (6) AI's role in integrated diagnostics, (7) pathology tasks likely to be fully automated using AI, and (8) regulatory/legal and (9) ethical aspects of AI integration in pathology. This systematic consensus study details the expected short-to-mid-term impact of AI on pathology practice. These findings provide timely and relevant information regarding future care delivery in pathology and raise key practical, ethical, and legal challenges that must be addressed prior to AI's successful clinical implementation. No specific funding was provided for this study.

Unrealistic Optimism Regarding Artificial Intelligence Opportunities in Human Resource Management

Artificial intelligence (AI) has many uses in domains like automotive and finance or business divisions like human resource management (HRM). This study presents a survey that was conducted among a German national sample (n = 79) of HRM personnel from small- and medium-sized enterprises regarding the expected impact of AI on their own and other companies. Indications for unrealistic optimism, i.e., assuming negative impacts are more likely for others than oneself, were identified. AI will play an increasingly important role, with cost reductions and efficiency gains serving as the highest motives and a lack of AI specialists representing the highest inhibitor. Participants assume that AI will reduce the number of employees in other companies, while it let the one in their own grow. They expect AI to take over more tasks in other companies and believe AI will more impact other companies' HRM, especially in administrative processing. Future research should include (repeated) investigations into other business divisions.

Voice orientation of conversational interfaces in vehicles

ABSTRACT Connected vehicles have become a promising platform for conversational agents. However, drivers might struggle to control systems that feature multiple AI agents. To improve the usability of increasingly complex systems and the user’s interaction experience, the integration of various conversational agents needs to be carefully considered. This study aims to investigate the following: What constitutes an efficient, user-friendly arrangement of the tasks performed by a home and a car artificial intelligence (AI) agent? What is the optimal method to trigger the use of such agents? A between-subjects factorial experiment was conducted with three types of AI agent setting (home AI generalist vs. vehicle AI generalist vs. home and vehicle AI specialists) and two activation methods (voice activation vs. push-to-talk activation). The results indicate that interacting with specialist AI agents enhances the perceived ease of use and the credibility of these agents. Furthermore, voice activation improves the social presence and attractiveness of an AI agent. The findings suggest that an AI agent that offers role specialization and a natural interaction method will improve drivers’ interaction experience.

What do university students know about Artificial Intelligence? Development and validation of an AI literacy test

Artificial Intelligence (AI) strongly influences our daily lives and work environments. To deal with the challenges as well as to pick up on the opportunities associated with AI, university students need to acquire a basic understanding of AI (so-called AI literacy). To design effective study programs that foster AI competencies, it is necessary to assess the state of students' AI literacy. While there already are some AI literacy tests available, many instruments focus on specific AI courses, rely primarily on self-assessment, or do not provide detailed psychometric information. This study aims to develop and validate an AI literacy test and to provide initial insights into the current state of AI literacy among German university students. We present a validated multiple-choice test to assess AI literacy in higher education. The results suggest that there is a significant variance in AI literacy among students, with most showing a fundamental understanding of AI. AI literacy is higher among students with a technical study background or prior experience in AI. The results imply that there is a need for effective AI courses for a broader audience of students who use AI in their daily lives and will use AI tools in their future careers. Furthermore, educators should consider the variance of students’ prior knowledge when designing AI courses.

Augmenting care in hepatocellular carcinoma with artificial intelligence

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related death worldwide and prognosis remains poor. The recent paradigm shifts in management algorithms of such patients have resulted in unique challenges in the early identification of HCC, prognosis, surgical outcomes, prioritization of potential transplant recipients, donor-recipient matching, and so on. In recent years, advancements in artificial intelligence (AI) capabilities have shown potential in HCC treatment. In this narrative review, we outline first the different types of AI models that are applied in clinical practice and then focus on the frontiers of AI research in the diagnosis, prognostication, and treatment of HCC, particularly in classification of indeterminate liver lesions, tumor staging, survival prediction, improving equity in transplant recipient selection, prediction of treatment response and prognosis. We show that US coupled with AI-driven predictive models can provide accurate noninvasive screening tools for early disease. While AI models applied to contrast-enhanced CT, MRI and PET studies may appear to have limited clinical utility in disease diagnosis and differentials, owing to their accuracy, we highlighted the importance of such models in predicting pathological findings preoperatively. Despite the availability of many accurate, sensitive, and specific AI algorithms that outperform traditional scoring systems, they have not been widely used in clinical practice. The challenges in AI application, including distributional shift and imbalanced data, lack of standardization, and the ‘black box’ phenomenon, are addressed here. The importance of AI in the future of HCC makes it important for clinicians to have a good understanding of different AI techniques, their benefits, and potential pitfalls.

Australian women's judgements about using artificial intelligence to read mammograms in breast cancer screening.

Mammographic screening for breast cancer is an early use case for artificial intelligence (AI) in healthcare. This is an active area of research, mostly focused on the development and evaluation of individual algorithms. A growing normative literature argues that AI systems should reflect human values, but it is unclear what this requires in specific AI implementation scenarios. Our objective was to understand women's values regarding the use of AI to read mammograms in breast cancer screening. We ran eight online discussion groups with a total of 50 women, focused on their expectations and normative judgements regarding the use of AI in breast screening. Although women were positive about the potential of breast screening AI, they argued strongly that humans must remain as central actors in breast screening systems and consistently expressed high expectations of the performance of breast screening AI. Women expected clear lines of responsibility for decision-making, to be able to contest decisions, and for AI to perform equally well for all programme participants. Women often imagined both that AI might replace radiographers and that AI implementation might allow more women to be screened: screening programmes will need to communicate carefully about these issues. To meet women's expectations, screening programmes should delay implementation until there is strong evidence that the use of AI systems improves screening performance, should ensure that human expertise and responsibility remain central in screening programmes, and should avoid using AI in ways that exacerbate inequities.

Advancing parallel programming integrating artificial intelligence for enhanced efficiency and automation

This article delves into the burgeoning integration of Artificial Intelligence (AI) in parallel programming, highlighting its potential to transform the landscape of computational efficiency and developer experience. We begin by exploring the fundamental role of parallel programming in modern computing and the inherent challenges it presents, such as task distribution, synchronization, and memory management. The advent of AI, especially in machine learning and deep learning, offers novel solutions to these challenges. We discuss the application of AI in automating the creation of parallel programs, with a focus on automatic code generation, adaptive resource management, and the enhancement of developer experience. The article examines specific AI methods – genetic algorithms, reinforcement learning, and neural networks – and their application in optimizing various aspects of parallel programming. Further, we delve into the prospects of combining these AI methods for a synergistic effect, emphasizing the potential for increased efficiency and accuracy. The importance of integrating AI technologies with existing development tools is also highlighted, aiming to bring AI's benefits to a broader developer audience. The article concludes with an outlook on future research directions, including the development of adaptable AI models tailored to diverse tasks and environments in parallel programming. These advancements promise to make parallel programming more powerful, accessible, and efficient, paving the way for a new era of computational capability and innovation.

Integration of Artificial Intelligence in the Work System Model According to REFA

Artificial intelligence (AI) is increasingly used in today's working world. For the design of hybrid work systems and associated manufacturing processes, data analyses, algorithms and visualizations can make decisive contributions. AI should support and relieve humans, but not replace them. The focus of AI support here is in the area of manual assembly and supply logistics. The REFA work system in connection with the periodic table of AI provides the basis. The developed methodology for workplace design is based on this. The development of specific AI elements is based on the examples of evaluation of work position and posture as well as identification of wrong components.

Is trust in artificial intelligence systems related to user personality? Review of empirical evidence and future research directions

Artificial intelligence (AI) refers to technologies which support the execution of tasks normally requiring human intelligence (e.g., visual perception, speech recognition, or decision-making). Examples for AI systems are chatbots, robots, or autonomous vehicles, all of which have become an important phenomenon in the economy and society. Determining which AI system to trust and which not to trust is critical, because such systems carry out tasks autonomously and influence human-decision making. This growing importance of trust in AI systems has paralleled another trend: the increasing understanding that user personality is related to trust, thereby affecting the acceptance and adoption of AI systems. We developed a framework of user personality and trust in AI systems which distinguishes universal personality traits (e.g., Big Five), specific personality traits (e.g., propensity to trust), general behavioral tendencies (e.g., trust in a specific AI system), and specific behaviors (e.g., adherence to the recommendation of an AI system in a decision-making context). Based on this framework, we reviewed the scientific literature. We analyzed N = 58 empirical studies published in various scientific disciplines and developed a “big picture” view, revealing significant relationships between personality traits and trust in AI systems. However, our review also shows several unexplored research areas. In particular, it was found that prescriptive knowledge about how to design trustworthy AI systems as a function of user personality lags far behind descriptive knowledge about the use and trust effects of AI systems. Based on these findings, we discuss possible directions for future research, including adaptive systems as focus of future design science research.

Challenges and best practices in corporate AI governance: Lessons from the biopharmaceutical industry

While the use of artificial intelligence (AI) systems promises to bring significant economic and social benefits, it is also coupled with ethical, legal, and technical challenges. Business leaders thus face the question of how to best reap the benefits of automation whilst managing the associated risks. As a first step, many companies have committed themselves to various sets of ethics principles aimed at guiding the design and use of AI systems. So far so good. But how can well-intentioned ethical principles be translated into effective practice? And what challenges await companies that attempt to operationalize AI governance? In this article, we address these questions by drawing on our first-hand experience of shaping and driving the roll-out of AI governance within AstraZeneca, a biopharmaceutical company. The examples we discuss highlight challenges that any organization attempting to operationalize AI governance will have to face. These include questions concerning how to define the material scope of AI governance, how to harmonize standards across decentralized organizations, and how to measure the impact of specific AI governance initiatives. By showcasing how AstraZeneca managed these operational questions, we hope to provide project managers, CIOs, AI practitioners, and data privacy officers responsible for designing and implementing AI governance frameworks within other organizations with generalizable best practices. In essence, companies seeking to operationalize AI governance are encouraged to build on existing policies and governance structures, use pragmatic and action-oriented terminology, focus on risk management in development and procurement, and empower employees through continuous education and change management.

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns.

Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)-infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590.

A review of the use of artificial intelligence methods in infrastructure systems

The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the growth of digitalisation and has the potential to enable the ‘system of systems’ approach required in increasingly complex infrastructure systems. This paper reviews the extent to which research in economic infrastructure sectors has engaged with fields of AI, to investigate the specific AI methods chosen and the purposes to which they have been applied both within and across sectors. Machine learning is found to dominate the research in this field, with methods such as artificial neural networks, support vector machines, and random forests among the most popular. The automated reasoning technique of fuzzy logic has also seen widespread use, due to its ability to incorporate uncertainties in input variables. Across the infrastructure sectors of energy, water and wastewater, transport, and telecommunications, the main purposes to which AI has been applied are network provision, forecasting, routing, maintenance and security, and network quality management. The data-driven nature of AI offers significant flexibility, and work has been conducted across a range of network sizes and at different temporal and geographic scales. However, there remains a lack of integration of planning and policy concerns, such as stakeholder engagement and quantitative feasibility assessment, and the majority of research focuses on a specific type of infrastructure, with an absence of work beyond individual economic sectors. To enable solutions to be implemented into real-world infrastructure systems, research will need to move away from a siloed perspective and adopt a more interdisciplinary perspective that considers the increasing interconnectedness of these systems.

Drivers’ Understanding of Artificial Intelligence in Automated Driving Systems: A Study of a Malicious Stop Sign

Automated Driving Systems (ADS), like many other systems people use today, depend on successful Artificial Intelligence (AI) for safe roadway operations. In ADS, an essential function completed by AI is the computer vision techniques for detecting roadway signs by vehicles. The AI, though, is not always reliable and sometimes requires the human’s intelligence to complete a task. For the human to collaborate with the AI, it is critical to understand the human’s perception of AI. In the present study, we investigated how human drivers perceive the AI’s capabilities in a driving context where a stop sign is compromised and how knowledge, experience, and trust related to AI play a role. We found that participants with more knowledge of AI tended to trust AI more, and those who reported more experience with AI had a greater understanding of AI. Participants correctly deduced that a maliciously manipulated stop sign would be more difficult for AI to identify. Nevertheless, participants still overestimated the AI’s ability to recognize the malicious stop sign. Our findings suggest that the public do not yet have a sufficiently accurate understanding of specific AI systems, which leads them to over-trust the AI in certain conditions.

Bias in algorithms of AI systems developed for COVID-19: A scoping review

To analyze which ethically relevant biases have been identified by academic literature in artificial intelligence (AI) algorithms developed either for patient risk prediction and triage, or for contact tracing to deal with the COVID-19 pandemic. Additionally, to specifically investigate whether the role of social determinants of health (SDOH) have been considered in these AI developments or not. We conducted a scoping review of the literature, which covered publications from March 2020 to April 2021. ​Studies mentioning biases on AI algorithms developed for contact tracing and medical triage or risk prediction regarding COVID-19 were included. From 1054 identified articles, 20 studies were finally included. We propose a typology of biases identified in the literature based on bias, limitations and other ethical issues in both areas of analysis. Results on health disparities and SDOH were classified into five categories: racial disparities, biased data, socio-economic disparities, unequal accessibility and workforce, and information communication. SDOH needs to be considered in the clinical context, where they still seem underestimated. Epidemiological conditions depend on geographic location, so the use of local data in studies to develop international solutions may increase some biases. Gender bias was not specifically addressed in the articles included. The main biases are related to data collection and management. Ethical problems related to privacy, consent, and lack of regulation have been identified in contact tracing while some bias-related health inequalities have been highlighted. There is a need for further research focusing on SDOH and these specific AI apps.

Soldering Data Classification with a Deep Clustering Approach: Case Study of an Academic-Industrial Cooperation

Modern industries still commonly use traditional methods to visually inspect products, even though automation has many advantages over the skills of human labour. The automation of redundant tasks is one of the greatest successes of Artificial Intelligence (AI). It employs human annotation and finds possible relationships between features within a particular dataset. However, until recently, this has always been the responsibility of AI specialists with a specific type of knowledge that is not available to the industrial domain experts. We documented the joint research of AI and domain experts as a case study on processing a soldering-related industrial dataset. Our image classification approach relies on the latent space representations of neural networks already trained on other databases. We perform dimensionality reduction of the representations of the new data and cluster the outputs in the lower dimension. This method requires little to no knowledge of the underlying architecture of neural networks by the domain experts, meaning it is easily manageable by them, supporting generalization to other use cases that can be investigated in future work. We also suggest a misclassification detecting method. We were able to achieve near-perfect test accuracy with minimal annotation work.