Human Artificial Intelligence Research Articles

Analytical modeling of perceived authenticity in AI assistants: application of PLS-predict algorithm and importance-performance map analysis

PurposeThe present study aimed to explore the consequences of perceived authenticity in artificial intelligence (AI) assistants and develop a serial-mediation architecture specifying causation of loyalty in human–AI relationships. It intended to assess the predictive power of the developed model based on a training-holdout sample procedure. It further attempted to map and examine the predictors of loyalty, strengthening such relationship.Design/methodology/approachPartial least squares structural equation modeling (PLS-SEM) based on bootstrapping technique was employed to examine the higher-order effects pertaining to human–AI relational intricacies. The sample size of the study comprised of 412 AI assistant users belonging to millennial generation. PLS-Predict algorithm was used to assess the predictive power of the model, while importance-performance analysis was executed to assess the effectiveness of the predictor variables on a two-dimensional map.FindingsA positive relationship was found between “Perceived Authenticity” and “Loyalty,” which was serially mediated by “Perceived-Quality” and “Animacy” in human–AI relational context. The construct “Loyalty” remained a significant predictor of “Emotional-Attachment” and “Word-of-Mouth.” The model possessed high predictive power. Mapping analysis delivered contradictory result, indicating “authenticity” as the most significant predictor of “loyalty,” but the least effective on performance dimension.Practical implicationsThe findings of the study may assist marketers to understand the relevance of AI authenticity and examine the critical behavioral consequences underlying customer retention and extension strategies.Originality/valueThe study is pioneer to introduce a hybrid AI authenticity model and establish its predictive power in explaining the transactional and communal view of human reciprocation in human–AI relationship. It exclusively provided relative assessment of the predictors of loyalty on a two-dimensional map.

Safety of Human–Artificial Intelligence Systems: Applying Safety Science to Analyze Loopholes in Interactions between Human Organizations, Artificial Intelligence, and Individual People

Loopholes involve misalignments between rules about what should be done and what is actually done in practice. The focus of this paper is loopholes in interactions between human organizations’ implementations of task-specific artificial intelligence and individual people. The importance of identifying and addressing loopholes is recognized in safety science and in applications of AI. Here, an examination is provided of loophole sources in interactions between human organizations and individual people. Then, it is explained how the introduction of task-specific AI applications can introduce new sources of loopholes. Next, an analytical framework, which is well-established in safety science, is applied to analyses of loopholes in interactions between human organizations, artificial intelligence, and individual people. The example used in the analysis is human–artificial intelligence systems in gig economy delivery driving work.

The Buffering Role of Workplace Mindfulness: How Job Insecurity of Human-Artificial Intelligence Collaboration Impacts Employees’ Work–Life-Related Outcomes

The Buffering Role of Workplace Mindfulness: How Job Insecurity of Human-Artificial Intelligence Collaboration Impacts Employees’ Work–Life-Related Outcomes

Human–AI resource relations in value cocreation in service ecosystems

PurposeApplications of artificial intelligence (AI), such as virtual and physical service robots, generative AI, large language models and decision support systems, alter the nature of services. Most service research centers on the division between human and AI resources. Less attention has been paid to analyzing the entangled resource relations and interactions between humans and AI entities. Thus, the purpose of this paper is to extend our metatheoretical understanding of resource integration and value cocreation by analyzing different human–AI resource relations in service ecosystems.Design/methodology/approachThe conceptual paper adapts a novel framework from postphenomenology, specifically cyborg intentionality. This framework is used to analyze what kinds of human–AI resource relations enable resource integration and value cocreation in service ecosystems.FindingsWe conceptualize seven different human–AI resource relations, namely background, embodiment, hermeneutic, alterity, cyborg, immersion and composite relation. The sociotechnical entangled perspective on human–AI resource relations challenges and reframes our understanding of interactions between humans and nonhumans in resource integration and value cocreation and the distinction between operant and operand resources in service research.Originality/valueOur primary contribution to researchers and service providers is dissolving the distinction between operant and operand resources. We present two foundational propositions. 1. Humans and AI become entangled value cocreating resources in inherently sociotechnical service ecosystems; and 2. Human and AI entanglements in value cocreation manifest through seven resource relations in inherently sociotechnical service ecosystems. Understanding the combinatorial potential of different human–AI resource relations enables service providers to make informed choices in service ecosystems.

Advancing Collective Intelligence in Human–AI Collaboration: Foundations for the COHUMAIN Framework

Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capabilities in many ways, how can we ensure that the sociotechnical system as a whole—comprising a complex web of hundreds of human–machine interactions—is exhibiting collective intelligence? Research on human–machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Integrating these diverse perspectives and methods is crucial at this juncture. To truly advance our understanding of this important and rapidly evolving area, we need frameworks to facilitate research that bridges disciplinary boundaries. This paper advocates for establishing an interdisciplinary research domain—Collective Human-Machine Intelligence (COHUMAIN). It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. To illustrate the approach we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, which articulates the critical processes underlying the emergence and functioning of collective intelligence in human–AI collaborations.

The Rise of Human–Machine Collaboration: Managers’ Perceptions of Leveraging Artificial Intelligence for Enhanced B2B Service Recovery

AbstractThis research analyses managers’ perceptions of the multiple types of artificial intelligence (AI) required at each stage of the business‐to‐business (B2B) service recovery journey for successful human–AI collaboration in this context. Study 1 is an exploratory study that identifies managers’ perceptions of the main stages of a B2B service recovery journey based on human–AI collaboration and the corresponding roles of the human–AI collaboration at each stage. Study 2 provides an empirical examination of the proposed theoretical framework to identify the specific types of intelligence required by AI to enhance performance in each stage of B2B service recovery, based on managers’ perceptions. Our findings show that the prediction stage benefits from collaborations involving processing‐speed and visual‐spatial AI. The detection stage requires logic‐mathematical, social and processing‐speed AI. The recovery stage requires logic‐mathematical, social, verbal‐linguistic and processing‐speed AI. The post‐recovery stage calls for logic‐mathematical, social, verbal‐linguistic and processing‐speed AI.

Artificial intelligence for teaching and learning in schools: The need for pedagogical intelligence

Artificial intelligence (AI) has been hailed for its potential to revolutionize teaching and learning practices. Undoubtedly, there has been development, but has it transferred to a new pedagogical trend? Indeed, research shows more tools, software, etc., built through AI, but there is still a limited understanding of its pedagogical impact. This review aims to assess whether AI has indeed led to new pedagogical trends in education using a Human Center AI framework. To accomplish this, a systematic review of research on the pedagogical applications of AI in K-12 contexts was conducted, following the PRISMA guidelines. The review involved an inductive coding analysis of a comprehensive search across WoS, Scopus, and EBSBU. From a pool of 3277 publications spanning 2019 to 2023, 183 papers met the inclusion criteria for detailed analysis. Six categories emerged: Behaviorism, Cognitivism, Constructivism, Social Constructivism, Experiential Learning, and Community of Practices. The findings of this research provide a promising perspective on synthesizing the results based on the pedagogical framework that describes AI implementation. While technological advancements have improved AI capabilities, the application of AI in education largely follows the same principles of previous technologies. This research suggests that the failure to transform education through AI stems from a lack of consideration of Gardner's proposed ninth intelligence type—pedagogical intelligence. Furthermore, this paper offers a critical analysis of the HCAI framework and proposes an adaptation called Pedagogical Centered AI (PCAI) for designing and using AI in K-12 education. Final discussions highlight the implications and future perspectives of AI in educational settings.

Exploring Motivators for Trust in the Dichotomy of Human—AI Trust Dynamics

This study analyses the dimensions of trust in artificial intelligence (AI), focusing on why a significant portion of the UK population demonstrates a higher level of trust in AI compared to humans. Conducted through a mixed-methods approach, this study gathered 894 responses, with 451 meeting the criteria for analysis. It utilised a combination of a six-step Likert-scale survey and open-ended questions to explore the psychological, sociocultural, and technological facets of trust. The analysis was underpinned by structural equation modelling (SEM) and correlation techniques. The results unveil a strong predilection for trusting AI, mainly due to its perceived impartiality and accuracy, which participants likened to conventional computing systems. This preference starkly contrasts with the scepticism towards human reliability, which is influenced by the perception of inherent self-interest and dishonesty in humans, further exacerbated by a general distrust in media narratives. Additionally, this study highlights a significant correlation between distrust in AI and an unwavering confidence in human judgment, illustrating a dichotomy in trust orientations. This investigation illuminates the complex dynamics of trust in the era of digital technology, making a significant contribution to the ongoing discourse on AI’s societal integration and underscoring vital considerations for future AI development and policymaking.

Modeling the Complex Interplay: Dynamics of Job Displacement and Evolution of Artificial Intelligence in a Socio-Economic Landscape

Modeling the Complex Interplay: Dynamics of Job Displacement and Evolution of Artificial Intelligence in a Socio-Economic Landscape

Exploring human–artificial intelligence interactions in a negative pragmatic trial of computer-aided polyp detection

Background and aimsThe progress of artificial intelligence (AI) in endoscopy is at a crossroads. The positive results of randomized controlled trials (RCT) of computer-aided detection (CADe) have not been replicated in multiple pragmatic CADe trials, including ours. This gap between efficacy and effectiveness remains to be understood. We surveyed and interviewed our trial’s colonoscopists to gain insight into human-AI interactions. MethodsWe used a sequential, mixed-methodology design. Following the trial, we administered Survey 1 focusing on attitudes and beliefs before and after trying CADe. The trial’s null results were disclosed, and we then administered Survey 2 and conducted open-ended interviews, focusing on reactions to the null results. Responses were analyzed overall and by baseline adenoma detection rate (ADR) tertile. We identified key themes utilizing thematic analysis and qualitative software. ResultsNearly all colonoscopists responded (22 and 21 of 24 [92%, 88%] for Surveys 1 and 2). Most (96%) regarded endoscopic ability as critical to their professional identity. Large majorities conveyed trust in and enthusiasm for AI before and after trying CADe (82-87%), and desired to have CADe available (72%). Nearly two-thirds (62%) were surprised by the null results. There were few differences by ADR. No unifying explanation for the null results emerged from surveys or individual interviews. Colonoscopists expressed a range of expectations for AI in endoscopy. ConclusionsLack of enthusiasm or mistrust of AI/CADe do not explain our pragmatic CADe trial’s null results. AI may need to target dimensions beyond optical recognition to realize its promise in endoscopy.

A longitudinal model of continued acceptance of conversational artificial intelligence

PurposeThe existing technology acceptance models have not yet investigated functional and motivational factors impacting trust in and use of conversational artificial intelligence (AI) by integrating the feedback and sequential updating mechanisms. This study challenged the existing models and constructed an integrated longitudinal model. Using a territory-wide two-wave survey of a representative sample, this new model examined the effects of hedonic motivation, social motivation, perceived ease of use, and perceived usefulness on continued trust, intended use, and actual use of conversational AI.Design/methodology/approachAn autoregressive cross-lagged model was adopted to test the structural associations of the seven repeatedly measured constructs.FindingsThe results revealed that trust in conversational AI positively affected continued actual use, hedonic motivation increased continued intended use, and social motivation and perceived ease of use enhanced continued trust in conversational AI. While the original technology acceptance model was unable to explain the continued acceptance of conversational AI, the findings showed positive feedback effects of actual use on continued intended use. Except for trust, the sequential updating effects of all the measured factors were significant.Originality/valueThis study intended to contribute to the technology acceptance and human–AI interaction paradigms by developing a longitudinal model of continued acceptance of conversational AI. This new model adds to the literature by considering the feedback and sequential updating mechanisms in understanding continued conversational AI acceptance.

Navigating the Future Landscape: Probing the Mechanism and Feeding Model of AI within Theatrical Arts

This scholarly article explores the application and impact of artificial intelligence (AI) in the realm of theatrical art creation, providing an analysis of the current state, challenges, and future development of the integration of AI and theatre. Through comprehensive case studies of various theatrical works both domestically and abroad, the article delineates four major “feeding” models of AI in theatre creation, discussing the specific applications and effects of these modes within the sphere of artistic creation. The symbiotic relationship between human creators and AI during the creative process is emphasized, envisioning the potential of AI as an innovative collaborator advancing theatrical artsalongside human creators. Despite present challenges confronting AI in theatrical creation, such as emotional depth and data dependency, the potential demonstrated in enhancing creative efficiency and expanding the scope of artistic expression is noteworthy.

The design of human-artificial intelligence systems in decision sciences: A look back and directions forward

The field of decision sciences is undergoing significant disruption and reinvention because of rapid advances in artificial intelligence (AI) technologies and the design of complex human-artificial intelligence systems (HAIS). The integration of human decision behaviors with cutting-edge AI capabilities is transforming business and society in irreversible ways. In this paper, we examine prior research published in Decision Support Systems that makes contributions to HAIS design science research (DSR). We define synergistic interactions among DSR, AI technology design, and human interaction design, which we use to specify the dimensions for an analysis of the DSS HAIS literature. We identify key challenges, leading to future research directions for the design of HAIS as solutions for complex decision science problems.

Balancing the scale: navigating ethical and practical challenges of artificial intelligence (AI) integration in legal practices

AbstractThe paper explores the integration of artificial intelligence in legal practice, discussing the ethical and practical issues that arise and how it affects customary legal procedures. It emphasises the shift from labour-intensive legal practice to technology-enhanced methods, with a focus on artificial intelligence's potential to improve access to legal services and streamline legal procedures. This discussion importantly highlights the ethical challenges introduced by the integration of Artificial Intelligence, with a specific focus on issues of bias and transparency. These ethical concerns become particularly paramount in the context of sensitive legal areas, including but not limited to, child custody disputes, criminal justice, and divorce settlements. It underscores the critical need for maintaining ethical vigilance, advocating for developing and implementing AI systems characterised by a profound commitment to ethical integrity. This approach is vital to guarantee fairness and uphold transparency across all judicial proceedings. The study advocates for a "human in the loop" strategy that combines human knowledge and AI techniques to mitigate biases and guarantee individualised legal results to ensure AI functions as a complement rather than a replacement, the paper concludes by emphasising the necessity of preserving the human element in legal practices.

A Scoping Literature Review of Artificial Intelligence in Epidemiology: Uses, Applications, Challenges and Future Trends

Artificial Intelligence (AI) has been applied to many human endeavors, and epidemiology is no exception. The AI community has recently seen a renewed interest in applying AI methods and approaches to epidemiological problems. However, a number of challenges are impeding the growth of the field. This work reviews the uses and applications of AI in epidemiology from 1994 to 2023. The following themes were uncovered: epidemic outbreak tracking and surveillance, Geo-location and visualization of epidemics data, Tele-Health, vaccine resistance and hesitancy sentiment analysis, diagnosis, predicting and monitoring recovery and mortality, and decision support systems. Disease detection received the most interest during the time under review. Furthermore, the following AI approaches were found to be used in epidemiology: prediction, geographic information systems (GIS), knowledge representation, analytics, sentiment analysis, contagion analysis, warning systems, and classification. Finally, the work makes the following findings: the absence of benchmark datasets for epidemiological purposes, the need to develop ethical guidelines to regulate the development of AI for epidemiology as this is a major issue impeding it’s growth, a concerted and continuous collaboration between AI and Epidemiology experts to grow the field, the need to develop explainable and privacy retaining AI methods for more secured and human understandable AI solutions.

Barriers and Enablers in Integrating AI into Human Resource Management Strategies: Maximizing Human Capital

Artificial intelligence (AI) has shaken the foundation of modern workplaces like never before and has induced digitized workstyles within the organisation. These furtherance in technology are generating significant interest among stakeholders to embrace AI in human resource management (HRM). Research and Development teams, analysts and practitioners are keen to investigate the sequel of AI in HR and their collaboration with gadget applications involving machine language, Data-science, Blockchain and Big Data. This study investigates HRM specific factors that are imbibed towards adoption of AI in extended HR based digital platform adopting a qualitative research design with an abductive approach. This research also investigates key enablers like optimistic, enthusiastic, and collaborative employees, strong digital enabled leadership, reliable HR meta-data, specialized HR partners, and well-rounded accountable AI ethics. The study also examines barriers towards awareness in AI adoption: the inability to have a timely internal audit pulse check of employees, their ability of emotional decision making, ineffective agile digital experts as well as external HR partners. On summarising, this study also contributes theory by providing a model that influences AI adoption and proposes ascending in welcoming unified theory of acceptance and use of innovative technology in the context of AI adoption in HR upskilling and reskilling ecosystems eventually. The study also contributes the anecdotes of best-in-class industrial HR practices with secured digital policy formulation to reimagine cybermated workplaces cubical. Maximising the human capital in the digital era be obliged in harmonious conglomerative human–AI enterprise making workplaces an eminent future-ready in the wake of productive and massive successful digital disruptions with efficacy.

Hybrid human-artificial intelligence system for early detection and classification of AMD from fundus image

Hybrid human-artificial intelligence system for early detection and classification of AMD from fundus image

Artificial intelligence – Human intelligence conflict and its impact on process system safety

In the Industry 4.0 revolution, industries are advancing their operations by leveraging Artificial Intelligence (AI). AI-based systems enhance industries by automating repetitive tasks and improving overall efficiency. However, from a safety perspective, operating a system using AI without human interaction raises concerns regarding its reliability. Recent developments have made it imperative to establish a collaborative system between humans and AI, known as Intelligent Augmentation (IA). Industry 5.0 focuses on developing IA-based systems that facilitate collaboration between humans and AI. However, potential conflicts between humans and AI in controlling process plant operations pose a significant challenge in IA systems. Human-AI conflict in IA-based system operation can arise due to differences in observation, interpretation, and control action. Observation conflict may arise when humans and AI disagree with the observed data or information. Interpretation conflicts may occur due to differences in decision-making based on observed data, influenced by the learning ability of human intelligence (HI) and AI. Control action conflicts may arise when AI-driven control action differs from the human operator action. Conflicts between humans and AI may introduce additional risks to the IA-based system operation. Therefore, it is crucial to understand the concept of human-AI conflict and perform a detailed risk analysis before implementing a collaborative system. This paper aims to investigate the following: 1. Human and AI operations in process systems and the possible conflicts during the collaboration. 2. Formulate the concept of observation, interpretation, and action conflict in an IA-based system. 3. Provide a case study to identify the potential risk of human-AI conflict.

Cognitive intelligence in industrial robots and manufacturing

The transition from manual to autonomous manufacturing processes, which has been propelled by consecutive industrial revolutions, is concurrently contingent upon advancements in artificial intelligence (AI). AI does have dependency on data, making it incompatible in handling industrial uncertainties. Human cognition, adept at navigating uncertain situations, presents a potential solution. This article aims to illuminate the synergy between human cognition and AI algorithms. It underscores the disparity between AI methods and cognitive requirements in manufacturing and robotics through a survey, also proposes methods to infuse cognition into machines and robots for autonomous learning. It outlines the significance of reinforcement learning as a cognitive algorithm, tracing its evolution and showcasing its application in guiding robots for pick-and-place operations in manufacturing.

Who Determines What Is Relevant? Humans or AI? Why Not Both?

A spectrum of human-artificial intelligence collaboration in assessing relevance.