Trust In Artificial Intelligence Research Articles

Human-AI interaction: Augmenting decision-making for IT leader's project selection

This research seeks to determine whether IT leaders are willing to leverage Artificial Intelligence (AI) recommendations to augment their decision-making when considering which IT projects to select based on the projects’ contribution towards organizational goals and objectives and the IT leaders’ level of knowledge and trust in AI. The research design utilized a quantitative survey of 113 IT leaders to understand their willingness to accept AI recommendations that augment their own decision-making while considering organizational criteria for selecting the best IT projects to invest in. AI recommendations were measured based on factors contained within a conceptual AI-driven online recommendation system, while decision-making was measured based on factors such as uncertainty, consequences of decisions, information & goals, motivation, and self-regulation that were designed and validated to examine certain aspects that influence decision-making. IT project contributions were measured based on relevance, risk, reasonableness, basis research return, and business return associated with the project. The research results showed the relationship between IT Project Contributions and IT leader decision was significant in that the leader's perception of IT project contributions towards organizational goals and objectives has a positive influence on the IT project selection decision. Unfortunately, the remaining hypothesized relationships showed no significant outcomes. The research results did not show that IT Leader Decisions may be influenced by AI recommendations, nor the greater knowledge of AI an IT Leader has, the stronger the relationship will be in support of or against AI recommendations, nor that the greater trust in AI an IT Leader has, the stronger the relationship will be in support of or against AI recommendations. This research will provide important insights to understand IT project selection and human-AI collaboration by IT leaders in organizations.

Employee learning and skilling in AI embedded organizations – predictors and outcomes

Purpose In this research article we aim to answer two research questions: - With ever increasing AI based integrations in organizations, it is imperative to understand how employees learn and adapt to these newer technologies? What is the impact of this learning and adaptation on AI-based employee productivity? Design/methodology/approach A survey-based research design has been conducted among the employees working in technology companies. Questionnaires were administered by the researchers physically using snowball based purposive sampling to reach the participants who are working in the AI driven technology companies. The data was analyzed using Structural Equation Modelling and numerous studies have previously used this approach for model fit and testing. Findings it was found that employee learning has significant positive mediational impact on the relationship between AI trust, knowledge sharing and AI skills and AI-employee collaboration. SEM results indicate that data fits the proposed model well. Practical implications findings indicate that as the trust in AI develops, employees are more interested in learning and increasing their understanding of AI driven work and increase their AI skills such as Machine learning and data analytics. To increase AI-employee collaborations and upskilling: organizations will need to foster AI training opportunities; make employees work on projects having AI based sociotechnical systems; create environment of knowledge sharing among employees on AI themes. Originality/value The use of Artificial Intelligence (AI) is ubiquitous, and more and more organizations are adopting AI in their operations which lead to massive scale changes in the functioning of organizations. These large-scale massive transformations are posing challenges and opportunities at the same time. At the same time, it created immense challenges for employees’ adjustment to the new learning and skilling requirements posed by AI (Sengupta et al., 2022). It is therefore expected that employees embrace the associated new technologies of AI such as big data analytics, Machine learning and Robotics.

The interplay between teachers’ trust in artificial intelligence and digital competence

AbstractThis study examines the relation between K-12 teachers’ trust in artificial intelligence (TAI), their knowledge of AI (KAI), and their digital competence (DC). It further examines the relation between TAI and age, sex, teaching experience and International Standard Classification of Education (ISCED) levels. The study employed a comprehensive and validated instrument and used a sample of 211 primary and secondary school teachers. The results show that there is a significant positive relation between all three variables and that KAI is a robust and substantial predictor of TAI. In the absence of KAI, the significant relation between DC and TAI ceases to exist. In addition, teachers with different levels of DC do not show significant differences in their attitudes towards AI. Results further show that TAI is independent of age, sex, teaching experience and ISCED level in this sample of teachers. The study contributes valuable insights into the complex interplay between teachers’ TAI, their KAI, and their DC, providing practical implications for policy, teacher preparation and professional development in the rapidly evolving landscape of AI integration in education.

How do employees form initial trust in artificial intelligence: hard to explain but leaders help

This study experimentally investigates initial trust formation in the organizational context of an artificial intelligence (AI) system in human resource management (HRM). Drawing on social exchange theory and leader‐member exchange theory, we identify factors that contribute to initial trust in AI through cognitive and affective processing from the perspective of employees in the Chinese context. An online survey (N = 426) was conducted with a 2 (explanation of AI: without vs with) × 2 (trust in leaders: low vs high) design. Our findings demonstrate that initial trust plays a crucial role in AI adoption, and a trustworthy leader increases employees' AI trust and intention to adopt. Providing AI's benefits and risks moderates initial trust and the pathway to adoption. Moreover, familiarity with AI's application in HRM and organizational collectivism is also beneficial. Our findings suggest that organizations should prioritize cultivating initial trust in AI with employee‐oriented strategies, including trusted leadership and supportive training resources.

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.

On trust in humans and trust in artificial intelligence: a study with samples from Singapore and Germany extending recent research

The AI revolution is shaping societies around the world. People interact daily with a growing number of products and services that feature AI integration. Without doubt rapid developments in AI will bring positive outcomes, but also challenges. In this realm it is important to understand if people trust this omni-use technology, because trust represents an essential prerequisite (to be willing) to use AI products and this in turn likely has an impact on how much AI will be embraced by national economies with consequences for the local work forces. To shed more light on trusting AI, the present work aims to understand how much the variables trust in AI and trust in humans overlap. This is important to understand, because much is already known about trust in humans, and if the concepts overlap, much of our understanding of trust in humans might be transferable to trusting AI. In samples from Singapore (n = 535) and Germany (n = 954) we could observe varying degrees of positive relations between the trust in AI/humans variables. Whereas trust in AI/humans showed a small positive association in Germany, there was a moderate positive association in Singapore. Further, this paper revisits associations between individual differences in the Big Five of Personality and general attitudes towards AI including trust.The present work shows that trust in humans and trust in AI share only small amounts of variance, but this further depends on culture (varying here from about 4-10 percent of shared variance). Future research should further investigate such associations but by also considering assessments of trust in specific AI-empowered-products and AI-empowered-services, where things might be different.

Revealing the source: How awareness alters perceptions of AI and human-generated mental health responses

In mental health care, the integration of artificial intelligence (AI) into internet interventions could significantly improve scalability and accessibility, provided that AI is perceived as being as effective as human professionals. This longitudinal study investigates the comparative perceptions of ChatGPT and human mental health support professionals across three dimensions: authenticity, professionalism, and practicality. Initially, 140 participants evaluated responses from both sources without knowing their origin, revealing that AI-generated responses were rated significantly higher across all dimensions. Six months later, the same cohort (n = 111) reassessed these messages with the source of each response disclosed, aiming to understand the impact of source transparency on perceptions and trust towards AI. The results indicate a shift in perception towards human responses, only in terms of authenticity (Cohen's d = 0.45) and reveal a significant correlation between trust in AI and its practicality rating (r = 0.25), but not with authenticity or professionalism. A comparative analysis between blind and informed evaluations revealed a significant shift in favour of human response ratings (Cohen's d = 0.42–0.57), while AI response ratings experienced minimal variation. These findings highlight the nuanced acceptance and role of AI in mental health support, emphasizing that the disclosure of the response source significantly shapes perceptions and trust in AI-generated assistance.

Exploring STEAM teachers’ trust in AI-based educational technologies: a structural equation modelling approach

In the rapidly evolving landscape of education, Artificial Intelligence (AI) has emerged as a transformative tool with the potential to revolutionize teaching and learning processes. However, the successful integration of AI in education depends on the trust and acceptance of teachers. This study addresses a significant gap in research by investigating the trust dynamics of 677 in-service Science, Technology, Engineering, Arts, and Mathematics (STEAM) teachers in Nigeria towards AI-based educational technologies. Employing structural equation modelling for data analysis, our findings reveal that anxiety, preferred methods to increase trust, and perceived benefits significantly influence teachers' trust in AI-based edtech. Notably, the lack of human characteristics in AI does not impact trust among STEAM teachers. Additionally, our study reports a significant gender moderation effect on STEAM teachers' trust in AI. These insights are valuable for educational policymakers and stakeholders aiming to create an inclusive, AI-enriched instructional environment. The results underscore the importance of continuous professional development programs for STEAM teachers, emphasizing hands-on experiences to build and sustain confidence in integrating AI tools effectively, thus fostering trust in the transformative potentials of AI in STEAM education.

Integrating Explainable Machine Learning in Clinical Decision Support Systems: Study Involving a Modified Design Thinking Approach.

Though there has been considerable effort to implement machine learning (ML) methods for health care, clinical implementation has lagged. Incorporating explainable machine learning (XML) methods through the development of a decision support tool using a design thinking approach is expected to lead to greater uptake of such tools. This work aimed to explore how constant engagement of clinician end users can address the lack of adoption of ML tools in clinical contexts due to their lack of transparency and address challenges related to presenting explainability in a decision support interface. We used a design thinking approach augmented with additional theoretical frameworks to provide more robust approaches to different phases of design. In particular, in the problem definition phase, we incorporated the nonadoption, abandonment, scale-up, spread, and sustainability of technology in health care (NASSS) framework to assess these aspects in a health care network. This process helped focus on the development of a prognostic tool that predicted the likelihood of admission to an intensive care ward based on disease severity in chest x-ray images. In the ideate, prototype, and test phases, we incorporated a metric framework to assess physician trust in artificial intelligence (AI) tools. This allowed us to compare physicians' assessments of the domain representation, action ability, and consistency of the tool. Physicians found the design of the prototype elegant, and domain appropriate representation of data was displayed in the tool. They appreciated the simplified explainability overlay, which only displayed the most predictive patches that cumulatively explained 90% of the final admission risk score. Finally, in terms of consistency, physicians unanimously appreciated the capacity to compare multiple x-ray images in the same view. They also appreciated the ability to toggle the explainability overlay so that both options made it easier for them to assess how consistently the tool was identifying elements of the x-ray image they felt would contribute to overall disease severity. The adopted approach is situated in an evolving space concerned with incorporating XML or AI technologies into health care software. We addressed the alignment of AI as it relates to clinician trust, describing an approach to wire framing and prototyping, which incorporates the use of a theoretical framework for trust in the design process itself. Moreover, we proposed that alignment of AI is dependent upon integration of end users throughout the larger design process. Our work shows the importance and value of engaging end users prior to tool development. We believe that the described approach is a unique and valuable contribution that outlines a direction for ML experts, user experience designers, and clinician end users on how to collaborate in the creation of trustworthy and usable XML-based clinical decision support tools.

Artificial intelligence in career development: a scoping review

ABSTRACT Artificial intelligence (AI) has received more scholarly attention over the past few years, evidenced by the increasing volume of literature on AI. However, most of our current knowledge about AI, including its applications, has been derived from studies situated in the context of automation, medicine, and business. This article is a response to acall for expanding AI research to understand its implications for people and their career development. The goal is to provide an updated, holistic understanding of existing research on AI in career development. Through a scoping review of 101 journal publications, this article offered three insights. First, AI has a double-edged sword effect on career development. While AI technology helps streamline career development processes in organisations, it can also disrupt a workplace by causing job insecurity. Secondly, with the unintended consequences, integrating AI into organisational practices can backfire, leading employees to explore alternate careers. Lastly, infusing AI into career development programmes has caused some ethical concerns. With increasing applications of AI, humans are now expected to trust technology-based output, even when such output contradicts their own knowledge and experience. Such blind trust in AI may hinder career progression by providing inaccurate information and advice to the workforce.

Trust in artificial intelligence, trust in engineers, and news media: Factors shaping public perceptions of autonomous drones through UTAUT2

Traditional trust in artificial intelligence (AI) scholarship has assumed trust as an attitude placed exclusively in the AI system. We offer a more articulated view of trust in AI, extending the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model by integrating news media attention, trust in AI system, trust in AI engineers, and attitude to examine factors predicting public's intention to use autonomous passenger drones. Based on an online survey of adult Singaporeans (N = 1002), results from hierarchical OLS regression analyses demonstrated the direct effects of news media attention, trust in AI system, and attitude, alongside several UTAUT2 constructs, on use intention. Results of subsequent mediation analyses using structural equation modelling between trust in AI system, trust in AI engineers and use intention through attitude were consistent with a partial and full mediation effect, respectively. Notably, both trust constructs were influenced by news media attention in comparable magnitudes. In this sense, trust placed in AI system can be seen separately from that placed in engineers responsible for AI development. Furthermore, we found support for including attitude in the UTAUT2 main model for emerging technologies in which information and practice remain largely inaccessible to the public. Theoretical and practical implications are discussed.

Investigating Trust in Human-AI Collaboration for a Speech-Based Data Analytics Task

Complex real-world problems can benefit from the collaboration between humans and artificial intelligence (AI) to achieve reliable decision-making. We investigate trust in a human-in-the-loop decision-making task, in which participants with background on psychological sciences collaborate with an explainable AI system for estimating one’s anxiety level from speech. The AI system relies on the explainable boosting machine (EBM) model which takes prosodic features as the input and estimates the anxiety level. Trust in AI is quantified via self-reported (i.e., administered via a questionnaire) and behavioral (i.e., computed as user-AI agreement) measures, which are positively correlated with each other. Results indicate that humans and AI depict differences in performance depending on the characteristics of the specific case under review. Overall, human annotators’ trust in the AI increases over time, with momentary decreases after the AI partner makes an error. Annotators further differ in terms of appropriate trust calibration in the AI system, with some annotators over-trusting and some under-trusting the system. Personality characteristics (i.e., agreeableness, conscientiousness) and overall propensity to trust machines further affect the level of trust in the AI system, with these findings approaching statistical significance. Results from this work will lead to a better understanding of human-AI collaboration and will guide the design of AI algorithms toward supporting better calibration of user trust.

Women’s attitudes and perspectives on the use of artificial intelligence in the assessment of screening mammograms

PurposeTo investigate attitudes and perspectives on the use of artificial intelligence (AI) in the assessment of screening mammograms among women invited to BreastScreen Norway. MethodAn anonymous survey was sent to all women invited to BreastScreen Norway during the study period, October 10, 2022, to December 25, 2022 (n = 84,543). Questions were answered on a 10-point Likert scale and as multiple-choice, addressing knowledge of AI, willingness to participate in AI studies, information needs, confidence in AI results and AI assisted reading strategies, and thoughts on concerns and benefits of AI in mammography screening. Analyses were performed using χ2 and logistic regression tests. ResultsGeneral knowledge of AI was reported as extensive by 11.0% of the 8,355 respondents. Respondents were willing to participate in studies using AI either for decision support (64.0%) or triaging (54.9%). Being informed about use of AI-assisted image assessment was considered important, and a reading strategy of AI in combination with one radiologist preferred. Having extensive knowledge of AI was associated with willingness to participate in AI studies (decision support; odds ratio [OR]: 5.1, 95% confidence interval [CI]: 4.1–6.4, and triaging; OR: 3.4, 95% CI: 2.8–4.0) and trust in AI’s independent assessment (OR: 6.8, 95% CI: 5.7, 8.3). ConclusionsWomen invited to BreastScreen Norway had a positive attitude towards the use of AI in image assessment, given that human readers are still involved. Targeted information and increased public knowledge of AI could help achieve high participation in AI studies and successful implementation of AI in mammography screening.

Is this AI sexist? The effects of a biased AI’s anthropomorphic appearance and explainability on users’ bias perceptions and trust

Biases in artificial intelligence (AI), a pressing issue in human-AI interaction, can be exacerbated by AI systems’ opaqueness. This paper reports on our development of a user-centered explainable-AI approach to reducing such opaqueness, guided by the theoretical framework of anthropomorphism and the results of two 3 × 3 between-subjects experiments (n = 207 and n = 223). Specifically, those experiments investigated how, in a gender-biased hiring situation, three levels of AI human-likeness (low, medium, high) and three levels of richness of AI explanation (none, lean, rich) influenced users’ 1) perceptions of AI bias and 2) adoption of AI’s recommendations, as well as how such perceptions and adoption varied across participant characteristics such as gender and pre-existing trust in AI. We found that comprehensive explanations helped users to recognize AI bias and mitigate its influence, and that this effect was particularly pronounced among females in a scenario where females were being discriminated against. Follow-up interviews corroborated our quantitative findings. These results can usefully inform explainable AI interface design.

The Impact of Expectation Management and Model Transparency on Radiologists’ Trust and Utilization of AI Recommendations for Lung Nodule Assessment on Computed Tomography: Simulated Use Study

Background Many promising artificial intelligence (AI) and computer-aided detection and diagnosis systems have been developed, but few have been successfully integrated into clinical practice. This is partially owing to a lack of user-centered design of AI-based computer-aided detection or diagnosis (AI-CAD) systems. Objective We aimed to assess the impact of different onboarding tutorials and levels of AI model explainability on radiologists’ trust in AI and the use of AI recommendations in lung nodule assessment on computed tomography (CT) scans. Methods In total, 20 radiologists from 7 Dutch medical centers performed lung nodule assessment on CT scans under different conditions in a simulated use study as part of a 2×2 repeated-measures quasi-experimental design. Two types of AI onboarding tutorials (reflective vs informative) and 2 levels of AI output (black box vs explainable) were designed. The radiologists first received an onboarding tutorial that was either informative or reflective. Subsequently, each radiologist assessed 7 CT scans, first without AI recommendations. AI recommendations were shown to the radiologist, and they could adjust their initial assessment. Half of the participants received the recommendations via black box AI output and half received explainable AI output. Mental model and psychological trust were measured before onboarding, after onboarding, and after assessing the 7 CT scans. We recorded whether radiologists changed their assessment on found nodules, malignancy prediction, and follow-up advice for each CT assessment. In addition, we analyzed whether radiologists’ trust in their assessments had changed based on the AI recommendations. Results Both variations of onboarding tutorials resulted in a significantly improved mental model of the AI-CAD system (informative P=.01 and reflective P=.01). After using AI-CAD, psychological trust significantly decreased for the group with explainable AI output (P=.02). On the basis of the AI recommendations, radiologists changed the number of reported nodules in 27 of 140 assessments, malignancy prediction in 32 of 140 assessments, and follow-up advice in 12 of 140 assessments. The changes were mostly an increased number of reported nodules, a higher estimated probability of malignancy, and earlier follow-up. The radiologists’ confidence in their found nodules changed in 82 of 140 assessments, in their estimated probability of malignancy in 50 of 140 assessments, and in their follow-up advice in 28 of 140 assessments. These changes were predominantly increases in confidence. The number of changed assessments and radiologists’ confidence did not significantly differ between the groups that received different onboarding tutorials and AI outputs. Conclusions Onboarding tutorials help radiologists gain a better understanding of AI-CAD and facilitate the formation of a correct mental model. If AI explanations do not consistently substantiate the probability of malignancy across patient cases, radiologists’ trust in the AI-CAD system can be impaired. Radiologists’ confidence in their assessments was improved by using the AI recommendations.

An Introductory Guide to Artificial Intelligence in Interventional Radiology: Part 1 Foundational Knowledge.

Artificial intelligence (AI) is rapidly evolving and has transformative potential for interventional radiology (IR) clinical practice. However, formal training in AI may be limited for many clinicians and therefore presents a challenge for initial implementation and trust in AI. An understanding of the foundational concepts in AI may help familiarize the interventional radiologist with the field of AI, thus facilitating understanding and participation in the development and deployment of AI. A pragmatic classification system of AI based on the complexity of the model may guide clinicians in the assessment of AI. Finally, the current state of AI in IR and the patterns of implementation are explored (pre-procedural, intra-procedural, and post-procedural).

Use and Application of Large Language Models for Patient Questions Following Total Knee Arthroplasty

BackgroundA consumer-focused health care model not only allows unprecedented access to information, but equally warrants consideration of the appropriateness of providing accurate patient health information. Nurses play a large role in influencing patient satisfaction following total knee arthroplasty (TKA), but they come at a cost. A specific natural language artificial intelligence (AI) model, ChatGPT (Chat Generative Pre-trained Transformer), has accumulated over 100 million users within months of launching. As such, we aimed to compare: (1) orthopaedic surgeons’ evaluation of the appropriateness of the answers to the most frequently asked patient questions after TKA; and (2) patients’ comfort level in answering their postoperative questions by using answers provided by arthroplasty-trained nurses and ChatGPT. MethodsWe prospectively created 60 questions based on the most commonly asked patient questions following TKA. There were 3 fellowship-trained surgeons who assessed the answers provided by arthroplasty-trained nurses and ChatGPT-4 to each of the questions. The surgeons graded each set of responses based on clinical judgment as: (1) “appropriate,” (2) “inappropriate” if the response contained inappropriate information, or (3) “unreliable,” if the responses provided inconsistent content. Patients’ comfort level and trust in AI were assessed using Research Electronic Data Capture (REDCap) hosted at our local hospital. ResultsThe surgeons graded 44 out of 60 (73.3%) responses for the arthroplasty-trained nurses and 44 out of 60 (73.3%) for ChatGPT to be “appropriate.” There were 4 responses graded “inappropriate” and one response graded “unreliable” provided by the nurses. For the ChatGPT response, there were 5 responses graded “inappropriate” and no responses graded “unreliable.” There were 136 patients (53.8%) who were more comfortable with the answers provided by ChatGPT compared to 86 patients (34.0%) who preferred the answers from arthroplasty-trained nurses. Of the 253 patients, 233 (92.1%) were uncertain if they would trust AI to answer their postoperative questions. There were 127 patients (50.2%) who answered that if they knew the previous answer was provided by ChatGPT, their comfort level in trusting the answer would change. ConclusionsOne potential use of ChatGPT can be found in providing appropriate answers to patient questions after TKA. At our institution, cost expenditures can potentially be minimized while maintaining patient satisfaction. Inevitably, successful implementation is dependent on the ability to provide information that is credible and in accordance with the objectives of both physicians and patients. Level of EvidenceIII.

Towards trustworthy AI: An analysis of the relationship between explainability and trust in AI systems

As artificial intelligence (AI) becomes increasingly integral to our lives, ensuring these systems are trustworthy and transparent is paramount. The concept of explainability has emerged as a crucial element in fostering trust within AI systems. Nevertheless, the dynamics between explainability and trust in AI are intricate and not fully comprehended. This paper delves into the nexus between explainability and trust in AI, offering perspectives on crafting AI systems that users can rely on. Through an examination of existing literature, we investigate how transparency, accountability, and human oversight influence trust in AI systems and assess how various explainability approaches contribute to trust enhancement. Utilizing a set of experiments, our research examines how different explanatory models impact users' trust in AI systems, revealing that the nature and quality of explanations have a significant influence on trust levels. Additionally, we scrutinize the balance between explainability and accuracy in AI systems, discussing its implications for the development of reliable AI. This study underscores the critical role of explainability in engendering trust in AI systems, providing guidance on the development of AI systems that are both transparent and trustworthy, thereby fostering confidence among users.

IMPACTS Homeostasis Trust Management System: Optimizing Trust in Human-AI Teams

Artificial Intelligence (AI) is becoming more ubiquitous throughout our lives. As our reliance on this technology increases, ensuring human operators maintain an adequate level of trust is integral to their safe and effective operations. To facilitate the appropriate level of operator trust in AI, a mechanism to continuously evaluate and calibrate human-AI trust is required. Such a Trust Management System (TMS) will be integral to developing trustworthy AI systems and thus enable collaborative and effective Human-AI Teaming (HAT) in future operations. This paper starts a review of the current state-of-the-art in trust research applicable to HAT, then summarizes the development and presents the IMPACTS (intention, measurability, performance, adaptivity, communication, transparency, security) homeostasis TMS. It is based on a dynamic and transactional trust framework and allows for continuous trust monitoring, managing, and behavior adjustment to ensure operator trust is calibrated.

Enhancing Trust in Healthcare: The Role of AI Explainability and Professional Familiarity

The integration of Artificial Intelligence (AI) in healthcare has been impeded by a significant issue: a lack of trust among healthcare professionals, stemming from the opacity of AI decision-making processes and a general unfamiliarity with AI technologies. This study investigates the impact of AI's explainability and healthcare professionals' familiarity with AI on their trust in AI applications within healthcare settings. Adopting a quantitative research methodology, the study utilized a structured questionnaire to gather data from a diverse group of healthcare professionals, including doctors, nurses, and administrators, across various hospitals and healthcare institutions in Pakistan. The research employed a stratified random sampling approach to ensure a comprehensive and representative data set. The results indicated a positive and significant relationship between AI explainability and trust in AI (Path Coefficient: 0.62, t-Value: 5.20), suggesting that clearer and more transparent AI decision-making processes enhance healthcare professionals' trust., Similarly, familiarity with AI was found to positively influence trust in AI (Path Coefficient: 0.48, t-Value: 4.35), highlighting the importance of exposure and understanding of AI systems among healthcare professionals. These findings have crucial implications for both AI developers and healthcare administrators. For AI developers, the emphasis must be on creating more transparent and interpretable AI systems. For healthcare administrators, the results suggest the need to invest in training and educational programs to increase professionals' familiarity with AI, thereby enhancing trust and acceptance. The study significantly contributes to the existing literature by empirically validating the importance of AI explainability and familiarity in building trust in AI within the healthcare context, especially in a developing country setting. For policymakers, these insights are critical in guiding strategies and policies aimed at effectively integrating AI into healthcare systems. By addressing the identified factors, healthcare sectors can better leverage AI's potential, leading to improved patient care and more efficient healthcare operations.