The risk of trust: AI narratives in breast cancer detection

BACKGROUND AI-driven medical chatbots allow patients to seek consultations without the constraints of time and space. Despite the rapid advancements and, in some cases, superior performance of AI medical chatbots compared to human physicians in specific domains, user hesitation persists. Furthermore, AI medical chatbots are still relatively new to patients in China. Understanding how patients' perceptions (AI versus human physicians) influence the trust-building process is crucial for the broader adoption of this technology. OBJECTIVE This study aims to explore how different perception (Robot and Human-like) of users build trust in AI medical chatbot. Moreover, this study examines the moderating role of privacy concern on trust in technology and trust in AI. METHODS PLS-MGA was adopted with data collected from 1547 participants, both online and offline, to examine the empirical results. RESULTS Perceived ease of use (β = .433, p < .001), privacy concern (β = .079, p = .006), and brand reputation (β = .264, p < .001) were positively associated with trust in technology and trust in brand. Trust in brand (β = .151, p < .001) positively influenced trust in technology, and trust in technology significantly predicted trust in AI across all dimensions: cognition (β = .20, p < .001), information (β = .19, p < .001), and behavior (β = .17, p < .001). Privacy concern moderated the relationship between trust in technology and trust in AI (β = .11, p = .001), information (β = .08, p < .001), and behavior (β = .09, p < .001). No significant differences were found for AI experience, health status, and perceived risk. However, the paths for perceived ease of use, trust in brand, and trust in technology (benevolence) were significantly stronger in the human group. CONCLUSIONS This study contributes both theoretically and practically by advancing the current understanding of the trust-building process in AI healthcare. It also examines the moderating effect of privacy concerns in the trust-building process within the AI healthcare context.

Trust transfer is a promising perspective on prevalent discussions about trust in AI-capable technologies. However, the convergence of AI with other technologies challenges existing theoretical assumptions. First, it remains unanswered whether both trust in AI and the base technology is necessary for trust transfer. Second, a nuanced view on trust sources is needed, considering the dual role of trust. To address these issues, we examine whether trust in providers and trust in technologies are necessary trust conditions. We conducted a survey with 432 participants in the context of autonomous vehicles and applied necessary condition analysis. Our results indicate that trust in AI technology and vehicle technology are necessary sources. In contrast, only vehicle providers represent a necessary source. We contribute to research by providing a novel perspective on trust in AI, applying a promising data analysis method to reveal necessary trust sources, and consider duality of trust in trust transfer.

Trust plays an important role in many Information Systems (IS)-enabled situations. Most IS research employs trust as a measure of interpersonal or person-to-firm relations, such as trust in a Web vendor or a virtual team member. Although trust in other people is important, this article suggests that trust in the Information Technology (IT) itself also plays a role in shaping IT-related beliefs and behavior. To advance trust and technology research, this article presents a set of trust in technology construct definitions and measures. We also empirically examine these construct measures using tests of convergent, discriminant, and nomological validity. This study contributes to the literature by providing: (a) a framework that differentiates trust in technology from trust in people, (b) a theory-based set of definitions necessary for investigating different kinds of trust in technology, and (c) validated trust in technology measures useful to research and practice.

Empirically understanding trust in medical technology

The COVID-19 pandemic accelerated the adoption and use of AI technologies to support the virtualisation of the workplace. While previous research showed that systems’ use critically depends on users’ trust, little is known about the development of trust in AI technologies. This research focuses on an AI chatbot as a type of organisational AI system and asks how and why employees’ trust towards an AI chatbot is formed and sustained. To answer the research questions, we conducted an interpretive single case study of a global organisation. The study identifies three types of trust experienced by AI chatbot users – emotional, cognitive and organisational – and develops a framework of experiential and sustained trust formation. It contributes to the information systems literature by demonstrating the critical importance of emotional and organisational trust in complementing cognitive trust, as well as the key design features that promote trust in AI chatbot use.

Reduction of patient trust in physicians when supported by artificial intelligence: results of a vignette study among patients

The following article develops an AI Trust Framework and Maturity Model (AI-TFMM) to improve trust in AI technologies used by Autonomous Human Machine Teams Systems (A-HMT-S). The framework establishes a methodology to improve quantification of trust in AI technologies. Key areas of exploration include security, privacy, explainability, transparency and other requirements for AI technologies to be ethical in their development and application. A maturity model framework approach to measuring trust is applied to improve gaps in quantifying trust and associated metrics of evaluation. Finding the right balance between performance, governance and ethics also raises several critical questions on AI technology and trust. Research examines methods needed to develop an AI-TFMM and validates it against a popular AI technology (Chat GPT). OpenAI's GPT, which stands for "Generative Pre-training Transformer," is a deep learning language model that can generate human-like text by predicting the next word in a sequence based on a given prompt. ChatGPT is a version of GPT that is tailored for conversation and dialogue, and it has been trained on a dataset of human conversations to generate responses that are coherent and relevant to the context. The article concludes with results and conclusions from testing the AI Trust Framework and Maturity Model (AI-TFMM) applied to AI technology. Based on these findings, this paper highlights gaps that could be filled with future research to improve the accuracy, efficacy, application, and methodology of the AI-TFMM.

Current literature and public discourse on “trust in AI” are often focused on the principles underlying trustworthy AI, with insufficient attention paid to how people develop trust. Given that AI systems differ in their level of trustworthiness, two open questions come to the fore: how should AI trustworthiness be responsibly communicated to ensure appropriate and equitable trust judgments by different users, and how can we protect users from deceptive attempts to earn their trust? We draw from communication theories and literature on trust in technologies to develop a conceptual model called MATCH, which describes how trustworthiness is communicated in AI systems through trustworthiness cues and how those cues are processed by people to make trust judgments. Besides AI-generated content, we highlight transparency and interaction as AI systems’ affordances that present a wide range of trustworthiness cues to users. By bringing to light the variety of users’ cognitive processes to make trust judgments and their potential limitations, we urge technology creators to make conscious decisions in choosing reliable trustworthiness cues for target users and, as an industry, to regulate this space and prevent malicious use. Towards these goals, we define the concepts of warranted trustworthiness cues and expensive trustworthiness cues, and propose a checklist of requirements to help technology creators identify appropriate cues to use. We present a hypothetical use case to illustrate how practitioners can use MATCH to design AI systems responsibly, and discuss future directions for research and industry efforts aimed at promoting responsible trust in AI.

Trust of Artificial Intelligence-Augmented Point-of-Care Ultrasound Among Pediatric Emergency Physicians

There has been a surge of interest in explainable artificial intelligence (XAI). It is commonly claimed that explainability is necessary for trust in AI, and that this is why we need it. In this paper, I argue that for some notions of trust it is plausible that explainability is indeed a necessary condition. But that these kinds of trust are not appropriate for AI. For notions of trust that are appropriate for AI, explainability is not a necessary condition. I thus conclude that explainability is not necessary for trust in AI that matters.

This paper explores the critical interplay between trust in AI technology and trust in its deployment, focusing on the gaps in interpersonal trust often overlooked in AI adoption. While AI enhances operational efficiency and decision-making, it struggles to replicate the emotional and psychological foundations of human trust. Drawing on interdisciplinary literature, the study highlights three trust dimensions—technological, organizational, and user-centric—and their misalignment, which fuels skepticism about AI reliability and ethical behavior. Despite AI's potential to transform industries, trust deficits persist due to concerns about transparency, accountability, and the emotional resonance of AI. The findings underscore that trust in AI must evolve as a complementary construct, bridging human and machine interactions without displacing interpersonal trust. The study proposes a framework emphasizing cultural and organizational adaptation, regulatory oversight, and user engagement to foster a balanced, sustainable AI.

The aim of this cross-sectional study was to investigate the factors associated with trust in AI algorithms used in the e-commerce industry in Romania. The motivation for conducting this analysis arose from the observation of a research gap in the Romanian context regarding this specific topic. The researchers utilized a non-probability convenience sample of 486 college students enrolled at a public university in Romania, who participated in a web-based survey focusing on their attitudes towards AI in e-commerce. The findings obtained from an ordinal logistic model indicated that trust in AI is significantly influenced by factors such as transparency, familiarity with other AI technologies, perceived usefulness of AI recommenders, and the students’ field of study. To ensure widespread acceptance and adoption by consumers, it is crucial for e-commerce companies to prioritize building trust in these new technologies. This study makes significant contributions to our understanding of how young consumers in Romania perceive and evaluate AI algorithms utilized in the e-commerce sector. The findings provide valuable guidance for e-commerce practitioners in Romania seeking to effectively leverage AI technologies while building trust among their target audience.

This paper explores the critical interplay between trust in AI technology and trust in its deployment, focusing on the gaps in interpersonal trust often overlooked in AI adoption. While AI enhances operational efficiency and decision-making, it struggles to replicate the emotional and psychological foundations of human trust. Drawing on interdisciplinary literature, the study highlights three trust dimensions—technological, organizational, and user-centric—and their misalignment, which fuels skepticism about AI reliability and ethical behavior. Despite AI's potential to transform industries, trust deficits persist due to concerns about transparency, accountability, and the emotional resonance of AI. The findings underscore that trust in AI must evolve as a complementary construct, bridging human and machine interactions without displacing interpersonal trust. The study proposes a framework emphasizing cultural and organizational adaptation, regulatory oversight, and user engagement to foster a balanced, sustainable AI.

As AI technology continues to evolve, it plays an increasingly significant role in everyday life and social governance. However, the frequent occurrence of issues such as algorithmic bias, privacy breaches, and data leaks has led to a crisis of trust in AI among the public, presenting numerous challenges to social governance. Establishing technical trust in AI, reducing uncertainties in AI development, and enhancing its effectiveness in social governance have become a consensus among policymakers and researchers. By comparing different types of AI, the paper proposes and conceptualizes the idea of trustworthy AI, then discusses its characteristics and its value and impact pathways in social governance. The analysis addresses how mismatches in technological trust can affect social stability and the advancement of AI strategies. The paper highlights the potential of trustworthy AI to improve the efficiency of social governance and solve complex social problems.

This research investigates how interactions with chatbots influence environmentally responsible behaviour, with a particular focus on the moderating effect of trust in AI and chatbot technologies. As chatbots increasingly play a role in promoting sustainability and influencing consumer choices, it becomes important to assess their effectiveness in driving eco-conscious actions. The proposed framework suggests that chatbot engagement has a positive impact on eco-friendly behaviour; however, this effect is significantly shaped by the level of trust users have in such technologies. Trust acts as a pivotal moderator, either strengthening or weakening the influence of chatbot interactions on user behaviour. This study contributes to the expanding domain of digital environmental communication by shedding light on the psychological and technological elements that impact sustainable behaviour in the digital era. The research follows a descriptive approach, utilizing a structured questionnaire and convenience sampling to collect data from participants across Kerala.