Multi-Level AI Trustworthiness Labels Scale Potential Users’ Perceptions and Evaluations of AI Products

Currently, artificial intelligence is entering a rapidly developing era. Artificial intelligence technology is growing rapidly. Artificial intelligence technology can already be applied to various fields of social life, and artificial intelligence products with artificial intelligence technology as the core are abundant. Artificial intelligence products have been integrated into the lives of ordinary people. Due to the autonomy of artificial intelligence products, they may work in an unmanned state at certain times, resulting in infringement liability that may cause personal or property damage to others during the operation of artificial intelligence products. There are two ways to pursue accountability: one is to directly pursue the infringement liability of artificial intelligence products, and the specific subject who caused the infringement of artificial intelligence products shall bear the infringement liability; another approach is to pursue the responsibility of the producer and seller of artificial intelligence products for their subsequent observation obligations. The reason is that the producer and seller of artificial intelligence products fail to fulfill their subsequent observation obligations, resulting in the failure to detect defects in the artificial intelligence products in a timely manner and resulting in subsequent observation obligations. The responsible parties are the producers and sellers of artificial intelligence products.

The digital age provides, on the one hand, extraordinary opportunities for modern information technologies, including artificial intelligence (AI) products; on the other hand, it creates new problems for humanity, for example, of a legal and ethical nature. In addition, AI products can be highly effective and attractive from a commercial point of view; and from the point of view of humanity as a whole, they can signify a huge problem. Thus, there is a need to balance the value of these products, taking into account their multiaspectual nature. The purpose of this study is to develop a conceptual approach to assessing and balancing the value of AI products. Balancing involves assessing the value of AI products, comparing it to some standards, and adjusting it based on the identified inconsistencies. It is found that balancing the value of AI products should take into account three areas of view and evaluation – individual, business, society (humanity). Such an integral view will provide coverage of the main levels that are affected by AI products. A method for assessing the value of AI products has been proposed, which takes into account the multiaspectual nature of product consideration for each area (individual, business, society) and the priority of each aspect. It is also proposed that in the future, it is artificial intelligence that should provide this assessment of the value of AI products, taking into account a significant amount of necessary information. A conceptual scheme has been formed that links the increase in the level of development of AI products with the level of their responsibility, especially for those products that are designed to evaluate and determine the necessary areas of harmonization and balancing of value. This will ensure the development and commercial use of AI products with risk minimization from various perspectives. It is evident that these results are only a conceptual approach, on the basis of which it is possible to develop specific algorithms for evaluating and balancing the value of AI products.

Several countries, including the UK, have approved commercial artificial intelligence (AI) products as medical devices to assist in the diagnosis of skin cancer. However, lack of robust real-world data has led to professional bodies recommending against their use outside research environments. The aim of this study was to assess the performance of an internationally proven deep neural network as a screening tool in patients referred from primary care on the suspected skin cancer pathway. Dermoscopic images were obtained from patients attending a community lesion imaging clinic following referral from primary care on the suspected skin cancer pathway. Ultimate diagnosis was based on histology where available, face-to-face clinical opinion if no histology was obtained, and the opinion of two dermatology specialists with over 10 years’ experience for patients discharged directly from teletriage without histology or face-to-face review. Images were assessed by the neural network and binary categorization was performed. The algorithm was set to detect invasive malignancies and premalignant conditions (actinic keratosis, Bowen disease, keratoacanthoma) and was trained to dismiss benign naevi, benign keratotic lesions (seborrhoeic keratosis, lichen planus-like keratosis, solar lentigines), dermatofibromata and benign vascular lesions. In total, 333 of 382 lesions from consecutive patients who gave research consent were included. Reasons for exclusion were pen artefact on dermoscopic image (26), lesion resolved prior to imaging clinic (eight) inappropriate site (six), poor image quality (five) and other (four). Ultimate diagnoses were basal cell carcinoma (62), squamous cell carcinoma (19), melanoma (11), other malignancy (one), benign keratotic (80), actinic keratosis (61), Bowen disease (20), benign naevus (17), benign vascular (nine), keratoacanthoma (eight) and other benign (45). Overall, 81 of 333 lesions (24.3%) were classified by the algorithm as benign and 252 (75.7%) were flagged as possibly malignant or premalignant. Sensitivity for cancer was 100% (93 of 93 detected). Sensitivity for premalignant conditions was 97% (86 of 89 detected). Specificity for all patients was 51.7% (78 of 151 benign lesions correctly classified). Specificity when removing the ‘other benign’ category, which is in the algorithm’s exclusion criteria and not part of its training, was 65.1% (71 of 109 lesions accurately categorized). Two actinic keratoses and one cases of Bowen disease were incorrectly classified as benign. The algorithm performed extremely well on this real-life case series and detected 100% of invasive malignancies. AI screening of suspected skin cancer referrals is likely to play a significant part in addressing future National Health Service capacity–demand mismatch and would have reduced the caseload requiring specialist opinion by 24.3% in this series. There is urgent need in the UK for further transparent clinical trials and open competition between AI products on large real-world datasets. Further stratification of benign and premalignant conditions may lead to greater benefit than binary classification.

Aim/Purpose: This paper aims to investigate and understand the intentions of management undergraduate students in Hangzhou, China, regarding the adoption of Artificial Intelligence (AI) technologies in their education. It addresses the need to explore the factors influencing AI adoption in the educational context and contribute to the ongoing discourse on technology integration in higher education. Background: The paper addresses the problem by conducting a comprehensive investigation into the perceptions of management undergraduate students in Hangzhou, China, regarding the adoption of AI in education. The study explores various factors, including Perceived Relative Advantage and Trialability, to shed light on the nuanced dynamics influencing AI technology adoption in the context of higher education. Methodology: The study employs a quantitative research approach, utilizing the Confirmatory Tetrad Analysis (CTA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) methodologies. The research sample consists of management undergraduate students in Hangzhou, China, and the methods include data screening, principal component analysis, confirmatory tetrad analysis, and evaluation of the measurement and structural models. We used a random sampling method to distribute 420 online, self-administered questionnaires among management students aged 18 to 21 at universities in Hangzhou. Contribution: This paper explores how management students in Hangzhou, China, perceive the adoption of AI in education. It identifies factors that influence AI adoption intention. Furthermore, the study emphasizes the complex nature of technology adoption in the changing educational technology landscape. It offers a thorough comprehension of this process while challenging and expanding the existing literature by revealing the insignificant impacts of certain factors. This highlights the need for an approach to AI integration in education that is context-specific and culturally sensitive. Findings: The study highlights students’ positive attitudes toward integrating AI in educational settings. Perceived relative advantage and trialability were found to impact AI adoption intention significantly. AI adoption is influenced by social and cultural contexts rather than factors like compatibility, complexity, and observability. Peer influence, instructor guidance, and the university environment were identified as pivotal in shaping students’ attitudes toward AI technologies. Recommendations for Practitioners: To promote the use of AI among management students in Hangzhou, practitioners should highlight the benefits and the ease of testing these technologies. It is essential to create communication strategies tailored to the student’s needs, consider cultural differences, and utilize the influence of peers and instructors. Establishing a supportive environment within the university that encourages innovation through policies and regulations is vital. Additionally, it is recommended that students’ attitudes towards AI be monitored constantly, and strategies adjusted accordingly to keep up with the changing technological landscape. Recommendation for Researchers: Researchers should conduct cross-disciplinary and cross-cultural studies with qualitative and longitudinal research designs to understand factors affecting AI adoption in education. It is essential to investigate compatibility, complexity, observability, individual attitudes, prior experience, and the evolving role of peers and instructors. Impact on Society: The study’s insights into the positive attitudes of management students in Hangzhou, China, toward AI adoption in education have broader societal implications. It reflects a readiness for transformative educational experiences in a region known for technological advancements. However, the study also underscores the importance of cautious integration, considering associated risks like data privacy and biases to ensure equitable benefits and uphold educational values. Future Research: Future research should delve into AI adoption in various academic disciplines and regions, employing longitudinal designs and qualitative methods to understand cultural influences and the roles of peers and instructors. Investigating moderating factors influencing specific factors’ relationship with AI adoption intention is essential for a comprehensive understanding.

This paper investigates electromagnetic compatibility (EMC) problems in artificial intelligence (AI) products and summarize corresponding solutions. Concept and theories of EMC are introduced in Section II, analysis of EMC problems in AI products basing on three elements of EMC is given in Section III, solutions are presented in Section IV, and conclusions are given in the final Section.

Artificial intelligence is widely used in healthcare. World leading scientific schools are experimenting with various machine learning models and trying to implement them into medical practice. In various fields of medicine, there are good solutions already exists that allow artificial intelligence products use as medical devices for making medical decisions. For example, artificial intelligence products received a special impetus to this during COVID-19 in radiology. For high-quality artificial intelligence training, high-quality datasets are needed. That’s why as in general artificial intelligence, the transition from a model-centric approach to a data-centric approach is gradually moving. The data quality for neural networks training is becoming more and more acute. There are many problems associated with the statistically reliable medical datasets collection. The article considers an innovative solution of recent years - synthetic datasets, which can solve many dataset quality problems for artificial intelligence training. Key factors affecting the complexity of medical data mining are analyzed and examples of synthetic datasets can reduce artificial intelligence development time, balance data and improve the quality of artificial intelligence products are presented.KeywordsArtificial intelligenceSynthetic datasetsMedical artificial intelligence products

Artificial intelligence products (AIPs) for older adults enhance the functions of traditional products and improve the quality of their lives. However, AIPs are not popular among this population, and limited attempts have been made to investigate these users’ negative tendencies regarding AIPs. This study explores the causes of avoidance and exit behaviors toward AIPs among older people from both a functional and socio-emotional mismatch perspective. Data were collected from 1102 older AIP users to verify the research model and hypotheses. The results indicate that perceived control and expectation disconfirmation affect the functional mismatch, while public stigma has the greatest impact on the socio-emotional mismatch. Furthermore, the results highlight a mixed influence of the functional and socio-emotional mismatches on negative behaviors. This study explores older people’s negative tendencies toward AIPs, comprehensively considering the functions of AIPs and the socio-emotions they evoke. Thus, it provides new empirical evidence for the systematic relationship between the functional mismatch and the socio-emotional mismatch and fills the research gap on the influence on the subsequent behaviors of older adults. Additionally, this study sheds light on the specific methods of designing, developing, and promoting AIPs.

The effect of medical artificial intelligence innovation locus on consumer adoption of new products

Aim. The work aimed to study the consequences of implementing products using artificial intelligence technologies in economic security systems.Objectives. The work seeks to analyze the development directions and results of artificial intelligence technologies; to assess the capabilities (beneficial effects) of these technologies in economic security systems of organizations; to identify specific threats to economic security, associated with the use of artificial intelligence technologies in organizations; to develop recommendations for mitigating these threats.Methods. The study employed comparative and retrospective analysis, economic-statistical and index methods, as well as special methods for studying economic security, expert-analytical and system approaches.Results. It has been established that the use of artificial intelligence in economic security systems can produce a significant beneficial effect. The advantages of its use in the field under consideration are associated with the fact that in formal analysis (from the standpoint of its rate, accuracy, consistency and taking into account the maximum possible number of factors), the artificial intelligence products surpass human capabilities. This results in a gradual replacement (moreover in the future) of human intelligence with artificial intelligence in the field under consideration, at least in cases of tasks that do not require non-standard, creative solutions, based sometimes on the paradoxical nature of human thinking. At the same time, such a development of events gives rise to a number of specific threats to economic security, namely “engagement” of many products using AI technologies; the possibility of manipulating information; loss of critical thinking skills by officials; the growing importance of cyberattacks.Conclusions. The development of digital technologies, including artificial intelligence technologies, has an ambiguous impact on economic security at all levels of the economic system. Overcoming the problems identified in the study requires activities in a number of fields, namely the formation of national products using artificial intelligence technologies, within the implementation of state policy to achieve technological sovereignty; strengthening the information component of systemic measures to ensure economic security; revision of approaches to the formation of competencies of specialists in the field of economic security, who use (or can potentially use) artificial intelligence products in their activities.

Objectives This study explored the familiarity, perceptions and confidence of Australian radiology clinicians involved in reading screening mammograms, regarding artificial intelligence (AI) applications in breast cancer detection. Methods Sixty-five radiologists, breast physicians and radiology trainees participated in an online survey that consisted of 23 multiple choice questions asking about their experience and familiarity with AI products. Furthermore, the survey asked about their confidence in using AI outputs and their preference for AI modes applied in a breast screening context. Participants' responses to questions were compared using Pearson's χ 2 test. Bonferroni-adjusted significance tests were used for pairwise comparisons. Results Fifty-five percent of respondents had experience with AI in their workplaces, with automatic density measurement powered by machine learning being the most familiar AI product (69.4%). The top AI outputs with the highest ranks of perceived confidence were 'Displaying suspicious areas on mammograms with the percentage of cancer possibility' (67.8%) and 'Automatic mammogram classification (normal, benign, cancer, uncertain)' (64.6%). Radiology and breast physicians preferred using AI as second-reader mode (75.4% saying 'somewhat happy' to 'extremely happy') over triage (47.7%), pre-screening and first-reader modes (both with 26.2%) (P < 0.001). Conclusion The majority of screen readers expressed increased confidence in utilising AI for highlighting suspicious areas on mammograms and for automatically classifying mammograms. They considered AI as an optimal second-reader mode being the most ideal use in a screening program. The findings provide valuable insights into the familiarities and expectations of radiologists and breast clinicians for the AI products that can enhance the effectiveness of the breast cancer screening programs, benefitting both healthcare professionals and patients alike.

Drawing on the analytic of the “colonial matrix of power” developed by Aníbal Quijano within the Latin American modernity/coloniality research program, this article theorises how a system of coloniality underpins the structuring logic of artificial intelligence (AI) systems. We develop a framework for critiquing the regimes of global labour exploitation and knowledge extraction that are rendered invisible through discourses of the purported universality and objectivity of AI. ​​Through bringing the political economy literature on AI production into conversation with scholarly work on decolonial AI and the modernity/coloniality research program, we advance three main arguments. First, the global economic and political power imbalances in AI production are inextricably linked to the continuities of historical colonialism, constituting the colonial supply chain of AI. Second, this is produced through an international division of digital labour that extracts value from majority world labour for the benefit of Western technology companies. Third, this perpetuates hegemonic knowledge production through Western values and knowledge that marginalises non-Western alternatives within AI’s production and limits the possibilities for decolonising AI. By locating the production of AI systems within the colonial matrix of power, we contribute to critical and decolonial literature on the legacies of colonialism in AI and the hierarchies of power and extraction that shape the development of AI today.

The development of artificial intelligence (AI) technology is often seen as the domain of engineers, but ultimately, it is the everyday consumer who uses it. This means that AI is not just an engineering challenge; it must also be understood from the perspectives of marketing and consumer behavior. In this study, we first use the CiteSpace knowledge mapping tool to analyze research trends and their evolution in the field of AI from a macro perspective. Then, from a micro perspective, we carefully select and review key literature to define the concept of AI, and further provide a comprehensive overview of AI applications in marketing. This includes (1) AI technology and product acceptance factors, (2) Algorithm avoiding and doubts about AI, (3) Industry-spcific application of AI, and (4) Consumer behavior and psychological on AI. Finally, based on existing research findings, we propose future research topics and directions that are necessary for the continued exploration of AI in this context. The insights from this study can inspire new research ideas related to AI for marketing and consumer scholars. Furthermore, it offers an opportunity for business marketers and government policymakers to consider AI not just as a technological issue, but from the perspective of the consumers who will ultimately adopt and interact with these technologies.

At present, although artificial intelligence technology is still in the stage of weak artificial intelligence, it depends on the development of digital computing, which breaks the monopoly position of human beings in the field of literary creation, and then the legal subject and behavior of copyright have intelligent tendency, which puts forward new problems in judicial practice. Through the analysis of the nature of AI-generated works and the distinction between different categories, this paper makes a concrete analysis of the copyright of AI-generated works, and then draws the conclusion that the copyright of AI-generated works belongs to people. By using the criterion of "contact + substantial similarity", it is concluded that the self-editing articles produced by artificial intelligence based on deep learning do not belong to infringement. The expression and utilization of manuscript washing in artificial intelligence products constitutes infringement and does not belong to fair use. Whether the use of the original material by the ideological use of manuscript washing constitutes infringement requires a case judgment.

Riding on the "east wind" of ChatGPT's explosion, the copyright issues involved in artificial intelligence products have once again become the focus of academic circles. At present, the number of artificial intelligence products is proliferating, and the lack of protection of artificial intelligence products under the copyright law has led to multiple risks such as untrue attribution of artificial intelligence products and irregular regulation of plagiarism of artificial intelligence products. The practical demand for copyright protection of artificial intelligence products needs urgent response from the legal level. As the logical starting point for granting copyright protection to artificial intelligence products, there are two kinds of differences in the determination of the originality of the products, namely the personality binding theory and the objective standard theory. From the perspectives of legislative spirit, copyright development trend and technical feasibility, it is justified, feasible and positively meaningful to adopt the latter to grant copyrightability to artificial intelligence products. Based on the evaluation of the value of the copyrightability of artificial intelligence products, the originality criterion is broken down into two major elements, namely, uniqueness and creativity, to build a factual judgment path for the copyrightability of artificial intelligence products, and then to realize the specific determination of the copyrightability of specific artificial intelligence products. This in turn gives rise to the dispute over the copyright ownership of artificial intelligence products. At this stage, allowing copyright rights and interests to be freely distributed among relevant subjects is an appropriate way to create the best benefits and maintain a balance of interests of all parties.

Artificial Intelligence and the Pathologist: Future Frenemies?