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Machine learning (ML) technologies have changed the paradigm of knowledge discovery in organizations and transformed traditional organizational learning to human-machine hybrid intelligent organizational learning. However, the general distrust among humans towards knowledge derived from machine learning has hindered effective knowledge exchange between humans and machines, thereby compromising the efficiency of human-machine hybrid intelligent organizational learning. To explore this issue, we used multi-agent simulation to construct a knowledge learning model of a human-machine hybrid intelligent organization with human-machine trust. The simulation showed that whether human-machine trust has a positive effect on knowledge level depends on the initial input and the magnitude of the effect depends on the human learning propensity (exploration and exploitation). When humans reconfigure machine learning excessively, whether human-machine trust has a positive effect on the knowledge level depends on human learning propensity (exploration and exploitation). Maintaining appropriate human-machine trust in turbulent environments assists humans in integrating diverse knowledge to meet changing knowledge needs. Our study extends the human-machine hybrid intelligence organizational learning model by modeling human-machine trust. It will assist managers in effectively designing the most economical level of human-machine trust, thereby enhancing the efficiency of human-machine collaboration in human-machine hybrid intelligent organization. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Design Methods Results Conclusion Impact Patient or Public Contribution This study examines how social influence, human–machine trust and perceived job stress affect nurses' behavioural intentions towards AI‐assisted care technology adoption from a new perspective and framework. It also explores the interrelationships between different types of social influence and job stress dimensions to fill gaps in academic literature.A quantitative cross‐sectional study.Five hospitals in Taiwan that had implemented AI solutions were selected using purposive sampling. The scales, adapted from relevant literature, were translated into Chinese and modified for context. Questionnaires were distributed to nurses via snowball sampling from May 15 to June 10, 2023. A total of 283 valid questionnaires were analysed using the partial least squares structural equation modelling method.Conformity, obedience and human–machine trust were positively correlated with behavioural intention, while compliance was negatively correlated. Perceived job stress did not significantly affect behavioural intention. Compliance was positively associated with all three job stress dimensions: job uncertainty, technophobia and time pressure, while obedience was correlated with job uncertainty.Social influence and human–machine trust are critical factors in nurses' intentions to adopt AI technology. The lack of significant effects from perceived stress suggests that nurses' personal resources mitigate potential stress associated with AI implementation. The study reveals the complex dynamics regarding different types of social influence, human–machine trust and job stress in the context of AI adoption in healthcare.This research extends beyond conventional technology acceptance models by incorporating perspectives on organisational internal stressors and AI‐related job stress. It offers insights into the coping mechanisms during the pre‐adaption AI process in nursing, highlighting the need for nuanced management approaches. The findings emphasise the importance of considering technological and psychosocial factors in successful AI implementation in healthcare settings.No Patient or Public Contribution. [ABSTRACT FROM AUTHOR]

Machine learning (ML) technologies have changed the paradigm of knowledge discovery in organizations and transformed traditional organizational learning to human-machine hybrid intelligent organizational learning. However, the general distrust among humans towards knowledge derived from machine learning has hindered effective knowledge exchange between humans and machines, thereby compromising the efficiency of human-machine hybrid intelligent organizational learning. To explore this issue, we used multi-agent simulation to construct a knowledge learning model of a human-machine hybrid intelligent organization with human-machine trust. The simulation showed that whether human-machine trust has a positive effect on knowledge level depends on the initial input and the magnitude of the effect depends on the human learning propensity (exploration and exploitation). When humans reconfigure machine learning excessively, whether human-machine trust has a positive effect on the knowledge level depends on human learning propensity (exploration and exploitation). Maintaining appropriate human-machine trust in turbulent environments assists humans in integrating diverse knowledge to meet changing knowledge needs. Our study extends the human-machine hybrid intelligence organizational learning model by modeling human-machine trust. It will assist managers in effectively designing the most economical level of human-machine trust, thereby enhancing the efficiency of human-machine collaboration in human-machine hybrid intelligent organization.

AbstractWith the development of intelligent transportation, it has become mainstream for drivers and automated systems to cooperate to complete train driving tasks. Human-machine trust has become one of the biggest challenges in achieving safe and effective human-machine cooperative driving. Accurate evaluation of human-machine trust is of great significance to calibrate human-machine trust, realize trust management, reduce safety accidents caused by trust bias, and achieve performance and safety goals. Based on typical driving scenarios of high-speed trains, this paper designs a train fault judgment experiment. By adjusting the machine’s reliability, the driver’s trust is cultivated to form their cognition of the machine. When the driver’s cognition is stable, data from the Trust in Automated (TIA) scale and modes of physiological information, including electrodermal activity (EDA), electrocardiograms (ECG), respiration (RSP), and functional near-infrared spectroscopy (fNIRS), are collected during the fault judgment experiment. Based on analysis of this multi-modal physiological information, a human-machine trust classification model for high-speed train drivers is proposed. The results show that when all four modes of physiological information are used as input, the random forest classification model is most accurate, reaching 93.14%. This indicates that the human-machine trust level of the driver can be accurately represented by physiological information, thus inputting the driver’s physiological information into the classification model outputs their level of human-machine trust. The human-machine trust classification model of high-speed train drivers built in this paper based on multi-modal physiological information establishes the corresponding relationship between physiological trust and human-machine trust level. Human-machine trust level is characterized by physiological trust monitoring, which provides support for the dynamic management of trust. [ABSTRACT FROM AUTHOR]

Based on Boyd’s “Observation Orientation-Decision-Action (OODA)” aerial combat theory and the principles of operational success, an analysis of the operational division patterns for cross-generational human-machine collaboration was conducted. The research proposed three stages in the development of aerial combat human-machine fusion intelligence: “Human-Machine Separation, Functional Coordination,” “Human-Machine Trust, Task Coordination,” and “Human-Machine Integration, Deep Fusion.” Currently, the transition from the first stage to the second stage is underway, posing challenges primarily related to the lack of effective methods guiding experimental research on human-machine fusion interaction and trust. Building upon the principles of decision neuroscience and the theory of supply and demand relationships, the study analyzed the decision-making patterns of human-machine fusion intelligence under different states. By investigating the correlations among aerial combat mission demands, dynamic operational limits of human-machine tasks, and aerial combat mission performance, a theoretical model of human-machine fusion interaction and trust was proposed. This model revealed the mechanistic coupling of human-machine interactions in aerial tasks, aiming to optimize the decision-making processes of human-machine systems to enhance mission performance. It provides methodological support for the design and application of intelligent collaborative interaction modes in aviation equipment. [ABSTRACT FROM AUTHOR]

As Artificial Intelligence (AI) proliferates across various sectors such as healthcare, transportation, energy, and military applications, the collaboration between human-AI teams is becoming increasingly critical. Understanding the interrelationships between system elements - humans and AI - is vital to achieving the best outcomes within individual team members' capabilities. This is also crucial in designing better AI algorithms and finding favored scenarios for joint AI-human missions that capitalize on the unique capabilities of both elements. In this conceptual study, we introduce Intentional Behavioral Synchrony (IBS) as a synchronization mechanism between humans and AI to set up a trusting relationship without compromising mission goals. IBS aims to create a sense of similarity between AI decisions and human expectations, drawing on psychological concepts that can be integrated into AI algorithms. We also discuss the potential of using multimodal fusion to set up a feedback loop between the two partners. Our aim with this work is to start a research trend centered on exploring innovative ways of deploying synchrony between teams of non-human members. Our goal is to foster a better sense of collaboration and trust between humans and AI, resulting in more effective joint missions.

Human–robot interaction (HRI) and game theory have developed distinct theories of trust for over three decades in relative isolation from one another. HRI has focused on the underlying dimensions, layers, correlates, and antecedents of trust models, while game theory has concentrated on the psychology and strategies behind singular trust decisions. Both fields have grappled to understand over-trust and trust calibration, as well as how to measure trust expectations, risk, and vulnerability. This article presents initial steps in closing the gap between these fields. By using insights and experimental findings from interdependence theory and social psychology, this work starts by analyzing a large game theory competition data set to demonstrate that the strongest predictors for a wide variety of human–human trust interactions are the interdependence-derived variables for commitment and trust that we have developed. It then presents a second study with human subject results for more realistic trust scenarios, involving both human–human and human–machine trust. In both the competition data and our experimental data, we demonstrate that the interdependence metrics better capture social “overtrust” than either rational or normative psychological reasoning, as proposed by game theory. This work further explores how interdependence theory – with its focus on commitment, coercion, and cooperation – addresses many of the proposed underlying constructs and antecedents within human–robot trust, shedding new light on key similarities and differences that arise when robots replace humans in trust interactions.

In robot-assisted vascular interventional surg- ery (VIS), surgeons often need to operate outside the operating room to avoid exposure to X-ray. However, it greatly changes the operating ways of surgeons, which affects judgment and operation safety. In this paper, a novel VIS robot system was developed to predict guidewire insertion states and operate collaboratively. To assist the surgeons in perceiving the insertion state, an insertion multi-states prediction model based on softmax logistic regression was proposed. Combined with the prediction model, a human-machine collaborative control strategy was designed, which allows surgeons to perceive the insertion states based on not only the force feedback constructed by the master side but also the prediction results from the slave side. Moreover, a human-machine trust evaluation model and a master-slave collaborative mapping model were proposed for improving safety and efficiency of surgery. To verify the effectiveness of these models, the evaluation experiments in the blood vessel model were carried out. It was indicated by the experiment results that the guidewire insertion states can be predicted by the prediction model in different environments, and the overall accuracy is 93%. The master-slave mapping ratio can be adjusted by the collaborative control strategy automatically to adapt to different surgical conditions. The experimental results showed the usability of the robot-assisted VIS system with the novel force-based perception method. [ABSTRACT FROM AUTHOR]

This study aimed to replicate Muir and Moray that demonstrated operators' trust in automated machines developing from faith, then dependability, and lastly predictability. Following the procedure of Muir and Moray, we asked undergraduate participants to complete a training program in a simulated pasteuriser plant and an experimental program including various errors in the pasteuriser. Results showed that the best predictor of overall trust was not faith but dependability, and that dependability consistently governed trust throughout the interaction with the pasteuriser. Thus, the obtained data patterns were inconsistent with those reported in Muir and Moray. We observed that operators in the current study used automatic control more frequently than manual control to successfully produce performance scores contrary to the operators in Muir and Moray. The results imply that dependability is a critical predictor of human-machine trust, which automation designer may focus on. More extensive future research using more modern automated technologies is necessary for understanding what factors control human-autonomy trust in modern ages. Practitioner Summary: The results suggest that dependability is a key factor that shapes human-machine trust across the time course of the trust development. This replication study suggests a new perspective for designing effective human-machine systems for untrained users who do not go through extensive training programs on automated systems. [ABSTRACT FROM AUTHOR]

Trust is a key determinant of whether people rely on automated systems in the military and the public. However, there is currently no standard for measuring trust in automated systems. In the present studies, we propose a scale to measure trust in automated systems that is grounded in current research and theory on trust formation, which we refer to as the Trust in Automated Systems Test (TOAST). We evaluated both the reliability of the scale structure and criterion validity using independent, military-affiliated and civilian samples. In both studies we found that the TOAST exhibited a two-factor structure, measuring system understanding and performance (respectively), and that factor scores significantly predicted scores on theoretically related constructs demonstrating clear criterion validity. We discuss the implications of our findings for advancing the empirical literature and in improving interface design. [ABSTRACT FROM AUTHOR]

With the successful introduction of advanced driver assistance systems, vehicles with driving automation technologies have begun to be released onto the market. Because the role of human drivers during automated driving may be different from the role of drivers with assistance systems, it is important to determine how general users consider such new technologies. The current study has attempted to consider driver trust, which plays a critical role in forming users' technology acceptance. In a driving simulator experiment, the demographic information of 56 drivers (50% female, 64% student, and 53% daily driver) was analyzed with respect to Lee and Moray's three dimensions of trust: purpose, process, and performance. The statistical results revealed that female drivers were more likely to rate higher levels of trust than males, and non-student drivers exhibited higher levels of trust than student drivers. However, no driving frequency-related difference was observed. The driver ratings of each trust dimension were neutral to moderate, but purpose-related trust was lower than process- and performance-related trust. Additionally, student drivers exhibited a tendency to distrust automation compared to non-student drivers. The findings present a potential perspective of driver acceptability of current automated vehicles. [ABSTRACT FROM AUTHOR]

Trust is a critical factor that drives successful human-automation interaction in a myriad of modern professional environments. One seminal work on human-automation trust is Muir and Moray (1996) showing that human-machine trust evolves from faith, then dependability, and finally predictability in a simulated supervisory control task. However, our recent work failed to replicate the finding of the original study, calling for further replication efforts. Experiment 1 aimed to fully replicate Muir and Moray (1996) where participants performed a simulated pasteurizer task. Experiment 2 attempted to replicate Experiment 1 using participants who major in Engineering as used in the original study. Both experiments showed that dependability was the best initial predictor of trust, building later to predictability and faith. Two experiments consistently failed to support both the hypothesis proposed by Muir and Moray (1996), that trust develops from predictability to dependability to faith, and their original findings that trust develops initially from faith. The results of the current experiments challenge this widely cited view of how human-machine trust develops. Modern automation designers should be aware that dependability might control initial trust development for general users and incorporate dependability information into their designs. [ABSTRACT FROM AUTHOR]