Assessment Of Trustworthiness Research Articles

Evaluation of Sabkha Soil Bearing Capacity by Plate Load Test in Al Muthanna Province

For projects such as airports and road paving, an appropriate foundation must be developed in Sabkha soil, which requires a trustworthy assessment of soil-bearing capacity. When heavy traffic is expected to result in substantial wheel loads throughout pavement construction and maintenance, the plate load test helps solve these issues with subgrade and sub-base layer design. This work aims to investigate and assess the geotechnical behavior regarding soil strata from one area in southern Iraq: Sabkha. Conversely, a comparison is made between subgrade response modulus and soil-bearing capacity determined by field plate load tests and traditional laboratory investigations. The data demonstrated that the values related to Ks in the consolidation test rose as a ratio of pre-consolidation pressure of 45% and dramatically dropped with an increase in applied stress that is vertically applied below pre-consolidation stress. Furthermore, PLT data demonstrated that when pressure was applied, the modulus of the subgrade reaction did not follow a regular pattern. At the beginning of the loaded stage, after the pre-consolidation stress, Ks values at testing points 2 and 3 were high. After that, Ks abruptly decreased, particularly when the applied pressure surpassed the pre-consolidation stress before being constant again. A detailed discussion is given on the effects of stress distribution and test conditions on the elasticity stress curve's shape and the subgrade reaction modulus.

Adaptable robots, ethics, and trust: a qualitative and philosophical exploration of the individual experience of trustworthy AI

AbstractMuch has been written about the need for trustworthy artificial intelligence (AI), but the underlying meaning of trust and trustworthiness can vary or be used in confusing ways. It is not always clear whether individuals are speaking of a technology’s trustworthiness, a developer’s trustworthiness, or simply of gaining the trust of users by any means. In sociotechnical circles, trustworthiness is often used as a proxy for ‘the good’, illustrating the moral heights to which technologies and developers ought to aspire, at times with a multitude of diverse requirements; or at other times, no specification at all. In philosophical circles, there is doubt that the concept of trust should be applied at all to technologies rather than their human creators. Nevertheless, people continue to intuitively reason about trust in technologies in their everyday language. This qualitative study employed an empirical ethics methodology to address how developers and users define and construct requirements for trust throughout development and use, through a series of interviews. We found that different accounts of trust (rational, affective, credentialist, norms based, relational) served as the basis for individual granting of trust in technologies and operators. Ultimately, the most significant requirement for user trust and assessment of trustworthiness was the accountability of AI developers for the outputs of AI systems, hinging on the identification of accountable moral agents and perceived value alignment between the user and developer’s interests.

Personality-Traits Taxonomy and Operational and Environmental Performance: A Cross-Sectional Analysis of Small and Medium Scale Manufacturing Enterprises

This research aimed to assess the operational and environmental performance of small- and medium-sized enterprises (SMEs) in Nigeria in relation to their adoption of personality-traits taxonomy (i.e., conscientiousness, openness to experience, extraversion, neuroticism or emotional resilience and agreeableness). The survey-based study involved the entire population of SME operators in South West Nigeria, totaling 1842 individuals (i.e., entrepreneurs). Through the use of stratified random-selection methods, a sample size of 420 was chosen. Data was collected, and both structural and measurement models were used to assess it. The results of the study demonstrate that personality traits have a significant influence on how successful SMEs function. The results also demonstrate that personality traits have a positive impact on SMEs’ environmental performance. These findings suggest that sustaining operational and environmental performance may be accomplished by resource allocation, promoting diversity and inclusion, and employing trustworthy assessment methods. To enhance both their overall operational performance and their environmental performance in particular, the study advises SME operators in Nigeria to undertake proper management of personality traits for sustainability. The paper recommends for greater research on this subject and emphasises the necessity of understanding how personality factors impact operational and environmental performance in Nigerian SMEs.

Oral health literacy among migrant mothers in Sweden. A qualitative study

ObjectiveThis interview study explored the oral health literacy of migrant mothers in Sweden, with special reference to where and why they access information about oral health and how they determine the credibility of such information.Material and methodIn-depth interviews were conducted with seven migrant mothers of children up to 10 years old. The mothers had entered Sweden from 2015 onwards and had been resettled in Kalmar County, Sweden. Their native language was Somalian, Dari or Arabic. The interview questions concerned the participants’ experiences of seeking oral health information, as well as oral health in general and dental health services. The interviews were analysed by qualitative content analysis.FindingsThe main findings indicate that migrant mothers used information sourcing as a pathway to solve oral health literacy tasks. Three main categories were identified, each with subcategories, describing the migrant mothers’ experiences of accessing and evaluating oral health information: ‘accessible source of information’, ‘needs-related purpose of seeking information’ and ‘trustworthiness assessment’. The migrant mothers reported that in case of a dental emergency or general queries, they sought oral health information from professionals and social sources. Moreover, they perceived oral health information to be most reliable when it was provided by dental professionals, was a recurring theme, or constituted majority opinion.ConclusionTo improve oral health literacy in migrant mothers of young children, it is important not only to provide consistent and recurrent oral health information through accessible information channels, but also to adapt dental care to be more culturally appropriate.

U-trustworthy Models. Reliability, Competence, and Confidence in Decision-Making

With growing concerns regarding bias and discrimination in predictive models, the AI community has increasingly focused on assessing AI system trustworthiness. Conventionally, trustworthy AI literature relies on the probabilistic framework and calibration as prerequisites for trustworthiness. In this work, we depart from this viewpoint by proposing a novel trust framework inspired by the philosophy literature on trust. We present a precise mathematical definition of trustworthiness, termed U-trustworthiness, specifically tailored for a subset of tasks aimed at maximizing a utility function. We argue that a model’s U-trustworthiness is contingent upon its ability to maximize Bayes utility within this task subset. Our first set of results challenges the probabilistic framework by demonstrating its potential to favor less trustworthy models and introduce the risk of misleading trustworthiness assessments. Within the context of U-trustworthiness, we prove that properly-ranked models are inherently U-trustworthy. Furthermore, we advocate for the adoption of the AUC metric as the preferred measure of trustworthiness. By offering both theoretical guarantees and experimental validation, AUC enables robust evaluation of trustworthiness, thereby enhancing model selection and hyperparameter tuning to yield more trustworthy outcomes.

Backdoor attack detection via prediction trustworthiness assessment

Backdoor attack aims to compromise clean models without arousing suspicion, in which poisoned models behave normally for clean inputs yet return adversary-desired results when triggers appear. Due to the great insidiousness and hazard of backdoor attacks, backdoor defences have been attracting a lot of attention in the machine learning security community. Apart from most backdoor mitigation defences, our defence aims to determine whether the prediction of the classifier is trustworthy. More specifically, we scrutinize whether the prediction result is determined by the adversary-defined trigger or the semantic information of an input. To accomplish this goal, we devise a novel algorithm named feature aggregation, which requires only benign inputs and aims to separate the feature representation distributions of poisoned inputs from those of benign ones. The feature aggregation minimizes the distance between intra-benign feature representations and maximizes the distance between benign and poisoned feature representations. Then, we employ flow-based probability density estimation to model the distribution of benign feature representations. Since the likelihood of poisoned inputs over the estimated distribution is significantly smaller than those of benign ones, they can be identified based on an adaptive threshold. Experimental results show that our method outperforms state-of-the-art defences.

The Next Era of Assessment: Building a Trustworthy Assessment System.

Assessment in medical education has evolved through a sequence of eras each centering on distinct views and values. These eras include measurement (e.g., knowledge exams, objective structured clinical examinations), then judgments (e.g., workplace-based assessments, entrustable professional activities), and most recently systems or programmatic assessment, where over time multiple types and sources of data are collected and combined by competency committees to ensure individual learners are ready to progress to the next stage in their training. Significantly less attention has been paid to the social context of assessment, which has led to an overall erosion of trust in assessment by a variety of stakeholders including learners and frontline assessors. To meaningfully move forward, the authors assert that the reestablishment of trust should be foundational to the next era of assessment. In our actions and interventions, it is imperative that medical education leaders address and build trust in assessment at a systems level. To that end, the authors first review tenets on the social contextualization of assessment and its linkage to trust and discuss consequences should the current state of low trust continue. The authors then posit that trusting and trustworthy relationships can exist at individual as well as organizational and systems levels. Finally, the authors propose a framework to build trust at multiple levels in a future assessment system; one that invites and supports professional and human growth and has the potential to position assessment as a fundamental component of renegotiating the social contract between medical education and the health of the public.

Purpose wins? A study of purpose-profit orientations, stakeholder perceptions and trust in firms

PurposeThe research seeks to evaluate stakeholder perceptions of firms, the extent these assessments impact trust in firms and possible implications for sustainability communications.Design/methodology/approachThree studies were undertaken involving two experiments (n = 436, n = 393) and one survey (n = 217). Analyses of variance was used in all three studies and in studies 2 and 3—to test for possible mediators—each variable was tested using Hayes' PROCESS macro (Hayes, 2013) with bootstrapping of 5,000 samples.FindingsResults demonstrate significant favouring of sustainability-minded firms. Some differences between consumers and investors were found but also notable commonalities such as a general propensity to favour purpose-oriented firms and similar determinations of trust in firms.Practical implicationsFindings could support more effective sustainability communications and firm decisions regarding investments in purpose- and sustainability-oriented initiatives. The results may also support designs to pursue and promote designations (e.g. B Corp) that legitimize sustainability claims.Originality/valueThis research was unique in its evaluation of two stakeholder types in the same context. Further, it provides new insights into how a firm’s profit-purpose orientation affects stakeholder perceptions and assessments of trustworthiness.

A novel framework for quantitative rhinoplasty evaluation by ResNet convolutional neural network

Rhinoplasty is a popular surgical operation, so proposing trustworthy assessment methods is crucial. Previous studies often utilized traditional or non-automatic methods for rhinoplasty evaluation, overlooked the aesthetic harmony of the nose with other facial features, and provided limited descriptions of facial beauty without detailed explanations. To address these limitations, we have developed a deep learning-based system for quantitative and qualitative facial beauty assessment and rhinoplasty results based on the random preoperative and postoperative color photographs of 376 patients, differentiating male and female faces. The quantitative evaluation includes automatically extracting 3D facial key points from frontal and lateral views, developing a novel mathematical 3D facial model, applying seven criteria from rhinoplasty literature, and assigning related scores. The qualitative evaluation comprises the design of a questionnaire, the extraction of facial features using a unique CNN-based algorithm, and the assignment of scores based on the questionnaire's results. Our method calculates the success percentage of rhinoplasty and provides precise and comprehensive quantitative and qualitative beauty scores. The accuracy of the proposed facial feature extraction network is 71 %, which is considered acceptable according to the complexity of defining beauty and the novelty of this work. All procedures and outcomes are verified by an ENT (Ear, Nose, and Throat) specialist. In particular, based on the presented extensive tables and histograms, some patients have lower postoperative scores than preoperative ones in some instances, which caused negative success scores. For this reason, individuals' appearance may occasionally worsen following rhinoplasty instead of improving. Therefore, preoperative assessments of facial features are crucial, and our proposed system facilitates this process. Our research also impacts individual self-assessment and surgeons' awareness significantly.

Validity of Test: English Lecturers perceptions of Test Validity in Intensive Reading Course' Final Exam at Universitas Prima Indonesia in academic year 2022/2023

This study was inspired from the researcher’ effort in order to enrich knowledge related to language testing’ component, one of component is validity and also curiosity to investigate the validity of Intensive Reading Final Test given by English lecturers in University of Prima Indonesia (UNPRI) in academic year 2022/2023. The research addresses two key questions: the lecturers’ perception of test validity in Intensive Reading Final Test in English Education Study Program of Teacher Training and Education Faculty of Universitas Prima Indonesia (UNPRI) and the reason of English lecturers in English Education Study Program of Teacher Training and Education Faculty of Universitas Prima Indonesia (UNPRI) Medan have such perceptions. This study applies qualitative research design in which involving 10 English lecturers at University of Prima Indonesia (UNPRI) as the respondents of this research, analyzing the perception of English lecturers related to validity of test in Intensive Reading Subject Final Exam. Findings shows that lecturers show their positive response to test validity with the information as follows: ALWAYS carry out an analysis of the question items before carrying out the Final Test” is in the percentage of 80%, “FREQUENTLY carry out crosschecks between basic competencies and indicators determined in the questions” is in the percentage of 100 %, “ALWAYS (60 %) and FREQUENTLY (20%) Carry out reliability/consistency measurements (reliability)”, “ALWAYS (60%) Providing feedback on test results given to students. Lecturers take such action in preparing final semester exam questions for Intensive reading courses are: Analyzing test items is crucial in creating effective assessments, Feedback on tests is crucial for students' learning and development, crosschecking between indicators and test items to make sure that educational tests are valid and reliable, the reliability of question items critical for producing accurate, consistent, and trustworthy assessment results. It supports the meaningful interpretation of scores and allows for valid comparisons across individuals or groups. A way to make a high-test validity, it can be done in some ways such as compile the test grid before writing and selecting items to be tested, and making or selecting items on the test based on standard competence, basic competence, learning objectives that have been formulated in the syllabus

Smartphone application for assessing teacher performance

This work seeks to design a smartphone application for assessing teacher performance utilizing a 4D development model modified with the Mardapi instrument development model of (1) define (defining); (2) design (deciding instrument requirements, determi­ning application media specifications, creating flowcharts, and creating storyboards); (3) develop (assemble instruments, code, execute tests, evaluate test results, and interpret measurement findings); and (4) disseminate (spread). Proof of the application’s viability was established through product testing with 26 principals acting as application users and evaluations by material and media specialists. The content validity of the acquired results was evaluated using the Aiken method, and the construct validity was evaluated using PLS (Partial Least Squares) analysis because the sample size does not have to be large, in accordance with this study where the number of samples is not so large. Using Cronbach’s alpha to evaluate the reliability. The results demonstrated that the built application was “possible,” as proven by the existing instruments in the valid and trustworthy application. This is proven by the fact that the content validity test utilizing the Aiken method yielded good validity results, with a mean score of 0.80 for material experts and 0.75 for media experts. The construct validity test using PLS (Partial Least Squares) yielded positive results for validity. The estimation of reliability using Cronbach’s alpha yields positive findings. Therefore, it can be concluded that the developed application for teacher performance evaluation possesses the features of a valid and trustworthy assessment instrument.

Trustworthiness assessment as an inclusion criterion for systematic reviews—What is the impact on results?

AbstractBackgroundThere is increasing concern that a significant proportion of randomized controlled trials (RCTs) included in Cochrane reviews may not be trustworthy. Applying the Cochrane Pregnancy and Childbirth Trustworthiness Screening Tool (CPC‐TST) has already had a clinically important effect on several reviews published by the Cochrane Pregnancy and Childbirth Group.ObjectivesWe wanted to assess the impact of removing untrustworthy RCTs from already‐published Cochrane reviews on a defined clinical area (antenatal and postnatal nutritional interventions).MethodsWe applied the tool to 18 Cochrane reviews (374 RCTs). The tool had four domains: (i) is the research governance trustworthy; (ii) are the baseline characteristics trustworthy; (iii) is the study feasible; (iv) are the results plausible? When additional information was needed, authors were contacted using a standard template. At least two attempts were made to contact the authors. At the end of the evaluation process each study was classified as: (i) included (YES to all questions); (ii) excluded (retracted study); or (iii) awaiting classification (any NO to the questions).ResultsNinety‐three out of 374 included studies (25%) were reclassified as “excluded” or “awaiting classification.” The number of included RCTs was reduced in 14 out of 18 reviews. Six reviews (33%) were judged to require updating because of important differences in the Summary of Findings tables (direction and size of effects and/or GRADE ratings), conclusions, implication for practice, and/or implication for research.ConclusionsFormal assessment of trustworthiness, and inclusion only of studies that satisfy prespecified criteria for trustworthiness, affect conclusions in a relatively large number of Cochrane reviews, with potentially important clinical implications for practice and research.

Changes in the Eurasian Otter (Lutra lutra) Population Including Assessment of Damage in the Last Decade: Is Their Protection Still Necessary?

Monitoring wildlife populations requires trustworthy assessment methods to get reliable data to be able to make reasonable management decisions. The Eurasian Otter (Lutra lutra) experienced a dramatic population decline throughout Central Europe during the twentieth century but is currently recovering in both distribution range and population size. Their numbers are regularly monitored by the relevant authorities, who issue an annual report on the occurrence of Eurasian Otters and the financial damage caused by them. These data for the last decade show that the abundance of Eurasian Otters in 2020 was 72% higher than in 2011 and that financial losses on fish farms has increased by 148% over the same period. In view of these facts, it is appropriate to discuss whether the Eurasian Otter is still an endangered species in Central Europe or whether the time has come for government regulation of their numbers, as has already happened with other initially protected animal, great cormorant (Phalacrocorax carbo).

What feels like news? Young people’s perceptions of news on Instagram

What do young people consider “news”? Now that news is dislocated from dedicated outlets of news organizations, it has become increasingly difficult to distinguish from other cultural forms, including entertainment, advertising and misinformation. Especially on visual social media, where many different forms, topics and tones circulate, so-called “news feeds” offer blends of content that only partially match traditional journalistic conceptualizations. This paper advances current conceptual debates around news(-ness), by going beyond what is culturally accepted and cognitively recognized as news. We make an argument for the importance of capturing young people’s affective and tacit understandings of news, by analyzing what feels like news to them on Instagram. These judgments matter because what users understand as news or non-news also affects their assessments of trustworthiness and reliability. Drawing upon a three-wave study (2020-2022) employing in-depth interviews with and walk-throughs of the Instagram feeds of N = 111 Dutch smartphone users (aged 16-25), we find that while young people are strongly aware of societal norms around what news is or should be, these cognitive understandings do not necessarily align with what they experience as news(-like) within their everyday practices. Although some users do employ traditional journalistic conceptualizations of news, others negotiate or challenge such definitions through processes of compartmentalization, homogenization or reconceptualization, to mitigate tensions between what they cognitively recognize versus what they affectively perceive as news. Consequently, we argue that more inclusive epistemological approaches are needed to comprehend young people’s shifting experiences of news and conceptualize news from an audience perspective.

What makes accidents severe! explainable analytics framework with parameter optimization

Most analytics models are built on complex internal learning processes and calculations, which might be unintuitive, opaque, and incomprehensible to humans. Analytics-based decisions must be transparent and intuitive to foster greater human acceptability and confidence in analytics. Explainable analytics models are transparent models in which the primary factors and weights that lead to a prediction can be explained. Typical AI models are non-transparent or opaque models, in which even the designers cannot explain how their models arrive at a specific decision. These transparent models help decision-makers understand their judgments and build trust in analytics. This study introduces an innovative, comprehensive model that fuses descriptive, predictive, and prescriptive analytics, offering a fresh perspective on car accident severity. Our methodological contribution lies in the application of advanced techniques to address data-related challenges, optimize feature selection, develop predictive models, and fine-tune parameters. Importantly, we also incorporate model-agnostic interpretation techniques, further enhancing the transparency and interpretability of our model, and separate explanations from models (i.e., model-agnostic interpretation techniques). Our findings should provide novel insights for a domain expert to understand accident severity. The explainable analytics approach suggested in this study supplements non-transparent machine learning prediction models, particularly optimized ensemble models. Our model's end product is a comprehensible representation of crash severity factors. To obtain a more trustworthy assessment of accident severity, this model may be supplemented with insurance data, medical data such as blood work and pulse rate, and previous medical history.

The Pros and Cons of Online Assessment in Higher Education

Nowadays, many universities and colleges use some combination of online and blended learning methods. Teachers need to re-evaluate assessment, student development, and instruction while working in non-traditional contexts. Questions like, "How does formative assessment work in an online learning environment?" and "How reliable and efficient is the assessment in achieving the set goals?" are examples of such concerns. It is of equal importance to consider how formative evaluation could enhance online pedagogical methods for instructors and students. The researchers in this study looks at the benefits of online formative assessment for teachers and their students. A mixed-methods survey was used to collect data regarding the formative assessment practices implemented in Google Classroom courses. Lecturers and students at a university made up the population. Several forms of formative evaluation make use of online tools, such as discussion forums and standardised tests. Greater student engagement, less time spent marking, and lower administrative expenses are a few of the more prominent benefits. There are additional benefits, such as more options for when and where to take the test. The key points are that students take a more active role in their own education and that teachers have the opportunity to create an environment where the focus is on both students and assessment by using tools like online formative assessment and formative feedback. In the context of online formative assessment, continuing trustworthy assessment tasks and interactive formative feedback are important components that will handle reasons and trustworthiness threats. Keywords: online formative assessments, student engagement, online pedagogical methods, learning, formative feedback, google classroom Proceedings Citation Format Ajogbeje, O.J. Boris, O.O. & Ekiugbo, U.E. (2023): The Pros and Cons of Online Assessment in Higher Education. Proceedings of the 37th iSTEAMS Multidisciplinary Cross-Border Conference. 30th October – 1st November, 2023. Academic City University College, Accra, Ghana. Pp 171-184. dx.doi.org/10.22624/AIMS/ACCRACROSSBORDER2023V2P2P33

Trustworthiness Assessment for Crowdsourcing-Based Citywide Parking Availability Sensing via Connected and Automated Vehicles

Real-time status acquisition of parking spaces is highly valuable for an intelligent urban parking system. Crowdsourcing-based parking availability sensing via connected and automated vehicles (CAVs) provides a feasible method with the advantages of high coverage and low cost. However, data trust issues arise from incorrect detection and incomplete information. This paper proposes a trustworthiness assessment method for crowdsourced CAV data considering different impact factors, such as the distance between the CAV and the target parking space, line abrasion, scene complexity, and image sharpness. The crowdsourced CAV data are collected through extensive field experiments and PreScan simulations. The classical line detection algorithm of VPS-Net and the target detection algorithm of YOLO-v3 are applied to detect on-street parking availability. A failure probability model based on the XGBoost algorithm is then developed to establish the relationship between data trustworthiness and different impact factors. The results show that the proposed model has an average accuracy of 78.29% and can effectively assess the degrees of external influences on the trustworthiness of the crowdsourced data. This paper provides a new tool to identify the data quality and improve the sensing accuracy for a crowdsourcing-based parking availability information system.

Lessons Learned from Assessing Trustworthy AI in Practice

Building artificial intelligence (AI) systems that adhere to ethical standards is a complex problem. Even though a multitude of guidelines for the design and development of such trustworthy AI systems exist, these guidelines focus on high-level and abstract requirements for AI systems, and it is often very difficult to assess if a specific system fulfills these requirements. The Z-Inspection® process provides a holistic and dynamic framework to evaluate the trustworthiness of specific AI systems at different stages of the AI lifecycle, including intended use, design, and development. It focuses, in particular, on the discussion and identification of ethical issues and tensions through the analysis of socio-technical scenarios and a requirement-based framework for ethical and trustworthy AI. This article is a methodological reflection on the Z-Inspection® process. We illustrate how high-level guidelines for ethical and trustworthy AI can be applied in practice and provide insights for both AI researchers and AI practitioners. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of real-world AI systems, as well as key recommendations and practical suggestions on how to ensure a rigorous trustworthiness assessment throughout the lifecycle of an AI system. The results presented in this article are based on our assessments of AI systems in the healthcare sector and environmental monitoring, where we used the framework for trustworthy AI proposed in the Ethics Guidelines for Trustworthy AI by the European Commission’s High-Level Expert Group on AI. However, the assessment process and the lessons learned can be adapted to other domains and include additional frameworks.

Betraying, Earning, or Justifying Trust in Health Organizations

AbstractHealth care and public health programs increasingly rely on, and often even require, organizational action, which is facilitated, if not dependent on, trust. Case examples in this essay highlight trust, trustworthiness, and distrust in public and private organizations, providing insights into how trust in health‐related organizations can be betrayed, earned, and justified and into the consequences of organizational trust and trustworthiness for the health of individuals and communities. These examples demonstrate the need for holistic assessments of trust in clinicians and trust in organizations and for organizations, public health, and the medical profession to address questions concerning their own trustworthiness. Normative and empirical assessments of trust and trustworthiness that capture the experiences of those treated within the walls of a health care organization, as well as the care of those outside, will contribute to more trustworthy systems of care.

The Dynamics of Trust: Path Dependence in Interpersonal Trust

Interpersonal trust is a critical factor in the success and effectiveness of organizations, influencing information sharing, decision-making, task distribution, and overall team performance. However, trust levels within organizations often exhibit a bimodal distribution, with some relationships characterized by high trust and others by low trust. This study seeks to understand the factors and dynamics contributing to this bimodal distribution and explore how leaders can positively influence the development of trust in their organizations. Using an abductive approach to theory development, we map artifacts from the interpersonal trust literature using DSRP to identify essential components for constructing a system dynamics model. Our analysis of the resultant model explores the dynamics of various interpersonal scenarios commonly encountered within professional organizations. We find that the initial assessment of trustworthiness plays an important role in the development of interpersonal trust and provides a leverage point for the resulting dynamics. The objective of this study is to provide leaders with a deeper understanding of interpersonal trust development, equipping them with the knowledge to effectively foster trust and positively impact their organizations.