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Lincoln and Guba provided some principles and four evaluative criteria called "trustworthiness" to guide social science research naturalistically. However, drawing on Science and Technology Studies (STS) literature, a field engaged with methods and practices of science for several decades, one can argue that this approach is not still fully naturalistic. In this paper, we review Lincoln and Guba's four criteria of trustworthiness from an STS perspective. We argue that the STS literature can challenge these criteria, but at the same time they have the potential to be reconstructed with the help of the materials STS provides. [ABSTRACT FROM AUTHOR]

The proposed Blockchain-Based Mitigation of Deauthentication Attacks (BBMDA) Framework aims to enhance the security and trustworthiness of IoT environments by leveraging blockchain technology, the Elliptic Curve Digital Signature Algorithm (ECDSA) for secure authentication, and Multi-Task Transformer (MTT) for efficient traffic classification. This paper presents a novel approach to mitigate de-authentication attacks in IoT ecosystems. The research methodology involves developing and implementing the BBMDA framework, followed by a comprehensive evaluation and comparison with existing techniques. Key findings indicate that the BBMDA framework outperforms traditional methods such as Support Vector Machine (SVM), k-nearest Neighbors (KNN), and Convolutional Neural Network (CNN) in terms of accuracy, false positive rate, false negative rate, precision, recall, and F1-score. These results underscore the effectiveness and efficiency of the proposed framework in enhancing IoT security. [ABSTRACT FROM AUTHOR]

AI-driven journalism refers to various methods and tools for gathering, verifying, producing, and distributing news information. Their potential is to extend human capabilities and create new forms of augmented journalism. Although scholars agreed on the necessity to embed journalistic values in these systems to make AI systems accountable, less attention was paid to data quality, while the results’ accuracy and efficiency depend on high-quality data in any machine learning task. Assessing data quality in the context of AI-driven journalism requires a broader and interdisciplinary approach, relying on the challenges of data quality in machine learning and the ethical challenges of using machine learning in journalism. To better identify these, we propose a data quality assessment framework to support the collection and pre-processing stages in machine learning. It relies on three of the core principles of ethical journalism—accuracy, fairness, and transparency—and participates in the shift from model-centric to data-centric AI, by focusing on data quality to reduce reliance on large datasets with errors, making data labelling consistent, and better integrating journalistic knowledge. [ABSTRACT FROM AUTHOR]

Abstract Cloud-based digital twins use real-time data from various data sources to simulate the behavior and performance of their physical counterparts, enabling monitoring and analysis. However, one restraining factor in the use of cloud computing for digital twins is its users’ concerns about the security of their data. This data may be located anywhere in the cloud, with very limited control of the user to ensure its security. Cloud-based digital twins provide opportunities for researchers to collaborate yet security of such digital twins requires measures specific to cloud computing. To overcome this shortcoming, we need to devise a mechanism that not only ensures essential security safeguards but also computes a Trustworthiness value for Cloud Service Providers (CSP). This would give confidence to cloud users and enable them to choose the right CSP for their data-related interaction. This research proposes a solution, whereby the Trustworthiness of CSPs is calculated based on their Compliance with data security controls, User Feedback, and Auditor Rating. Two additional factors, Accuracy of Compliance Measurement and Control Significance Factor have been built in, to cater for other nonstandard conditions. Our implementation of Data Security Compliance Monitor and Data Trust as a Service, along with three CSPs, each with ten different settings, has supported our proposition through the devised formula. Experimental outcomes show changes in the trustworthiness value with changes in compliance level, user feedback and auditor rating. CSPs with better compliance have better trustworthiness values. However, if the Accuracy of Compliance Measurement and Control Significance Factor are low the trustworthiness is also proportionately less. This creates a balance and realism in our calculations. This model is unique and will help in creating users’ trust in cloud-based digital twins.

Abstract Impressions of trustworthiness are formed quickly from faces. To what extent are these impressions shared among observers of the same or different races? Although high consensus of trustworthiness evaluation has been consistently reported, recent studies suggested substantial individual differences. For instance, negative implicit racial bias and low contact experience towards individuals of the other race have been shown to be related to low trustworthiness judgments for other-race faces. This pre-registered study further examined the effects of implicit social bias and experience on trustworthiness judgments of other-race faces. A relatively large sample of White (N = 338) and Black (N = 299) participants completed three tasks: a trustworthiness rating task of faces, a race implicit association test, and a questionnaire of experience. Each participant rated trustworthiness of 100 White faces and 100 Black faces. We found that the overall trustworthiness ratings for other-race faces were influenced by both implicit bias and experience with individuals of the other-race. Nonetheless, when comparing to the own-race baseline ratings, high correlations were observed for the relative differences in trustworthiness ratings of other-race faces for participants with varied levels of implicit bias and experience. These results suggest differential impact of social concepts (e.g., implicit bias, experience) vs. instinct (e.g., decision of approach-vs-avoid) on trustworthiness impressions, as revealed by overall vs. relative ratings on other-race faces.

Abstract Facial features are important sources of information about perceived trustworthiness. Masks and protective clothing diminish the visibility of facial cues by either partially concealing the mouth and nose or covering the entire face. During the pandemic, the use of personal protective equipment affected and redefined who trusts whom in society. This study used the classical investment game of interpersonal trust with Chinese participants to explore the impact of occlusion on interpersonal trust. Faces with moderate initial trustworthiness were occluded by a mask or protective clothing in Experiment 1 and were digitally occluded by a square in Experiment 2, and faces with three levels of initial trustworthiness were occluded by a mask in Experiment 3. Results showed that both undergraduates (Experiment 1a) and non-student adults (Experiment 1b) perceived the faces with protective clothing as more trustworthy than faces wearing standard masks and faces not wearing masks. Faces with the top halves showing were perceived as trustworthy as full faces, while faces with the bottom halves showing were perceived as less trustworthy. The effect of masks is weak and complex. Masks reduced participants’ trust in faces with high initial trustworthiness, had no effect on faces with low and moderate initial trustworthiness, and only slightly increased the trust of undergraduates in faces with moderate initial trustworthiness. Our findings indicate that the lack of information caused by occlusion and the social significance associated with occlusion collectively affect people’s trust behavior in Chinese society. We believe the findings of this study will be useful in elucidating the effects of personal protective equipment usage on perceptions of trustworthiness.

Livestreaming commerce research has grown amazingly and gained tremendous attention; however, a comprehensive model that explains and reveals customers' trustworthiness in livestreaming commerce is still absent and rarely studied. The present study seeks to capture the critical determinants that affect customers' trustworthiness in livestreaming commerce. In addition, this study is among the first to examine customer confidence in livestream commerce by investigating the influence of marketing influencers such as expertise, trustworthiness, and attractiveness and situational factors such as shopping motivation, time pressure, and product involvement on customers' trustworthiness in livestream commerce. The present study targeted Malaysian users of livestream commerce channels, and 562 valid responses were collected for analysis. The partial least squares path modeling (PLS-SEM) revealed influential factors represented by expertise, trustworthiness, and attractiveness, which are positively related to retailers' confidence in platforms that support livestreaming. At the same time, situational factors such as shopping motivation, time pressure, and product involvement represent factors that motivate the trustworthiness of livestream platforms. There is also an influence on the similarity factors between influencers and livestream commerce customers, which are represented by homophily and authenticity. Hence, live streamers provide real-time information and interactions that increase the proximity between streamers and customers. The five solutions captured from the Fuzzy Set Qualitative Comparative Analysis (fsQCA) analysis revolved around the importance of five basic elements, namely time pressure, trustworthiness, attractiveness, authenticity, and trust in streamer, to boost customers' trustworthiness in livestreaming commerce. The results of the present study provide insightful practical and theoretical implications for practitioners and academics that can benefit the livestreaming business and advance the trustworthiness of livestreaming platforms by emphasizing fun and entertaining experiences that arouse the emotions of customers and marketing influencers. [ABSTRACT FROM AUTHOR]

AbstractBackgroundAimsMethodsResultsConclusionSurvivors of Child Sexual Abuse (CSA) are reported to have difficulties in trusting. Yet no previous study investigating CSA survivors’ subjective experiences of trust exists and there is a paucity of clinical research into constructs and definitions of “trust.”To use a phenomenological lens to investigate CSA survivors’ descriptions of trust relationships and trustworthy others by privileging their subjective experience. To better understand how trust can be built within therapeutic relationships.A qualitative methodology using Interpretative Phenomenological Analysis was conducted within the survivor-research paradigm. The researcher was a person with lived experience of CSA who co-produced the study with CSA survivor advisors and co-constructed interviews with 17 adult CSA survivors.Findings present a “Survivor Trust Enactment Model” that delineates the process of building/repairing relational trust and advancing “transactional trust.” Trust is portrayed as nuanced and formed across and according to context, including the demarcation of generalised and relational trust. The findings emphasise that trustees’ trustworthiness is key to building trust which challenges assumptions that survivors are deficient in trust.The foregrounding of subjective trust experiences challenges diagnostic and clinical views on trust deficiency in adult CSA survivors. The study develops clinical constructs of trust, considers implications for clinical practice, and indicates areas for further research into trust dynamics in therapeutic relationships. [ABSTRACT FROM AUTHOR]

Trustworthiness assessment is an essential step to assure that interdependent systems perform critical functions as anticipated, even under adverse conditions. In this paper, a holistic trustworthiness assessment framework for ultra-wideband self-localization is proposed, including the attributes of reliability, security, privacy, and resilience. Our goal is to provide guidance for evaluating a system's trustworthiness based on objective evidence, i.e., so-called trustworthiness indicators. These indicators are carefully selected through the threat analysis of the particular system under evaluation. Our approach guarantees that the resulting trustworthiness indicators correspond to chosen real-world threats. Moreover, experimental evaluations are conducted to demonstrate the effectiveness of the proposed method. While the framework is tailored for this specific use case, the process itself serves as a versatile template, which can be used in other applications in the domains of the Internet of Things or cyber–physical systems. [ABSTRACT FROM AUTHOR]

Cloud-based digital twins use real-time data from various data sources to simulate the behavior and performance of their physical counterparts, enabling monitoring and analysis. However, one restraining factor in the use of cloud computing for digital twins is its users' concerns about the security of their data. This data may be located anywhere in the cloud, with very limited control of the user to ensure its security. Cloud-based digital twins provide opportunities for researchers to collaborate yet security of such digital twins requires measures specific to cloud computing. To overcome this shortcoming, we need to devise a mechanism that not only ensures essential security safeguards but also computes a Trustworthiness value for Cloud Service Providers (CSP). This would give confidence to cloud users and enable them to choose the right CSP for their data-related interaction. This research proposes a solution, whereby the Trustworthiness of CSPs is calculated based on their Compliance with data security controls, User Feedback, and Auditor Rating. Two additional factors, Accuracy of Compliance Measurement and Control Significance Factor have been built in, to cater for other nonstandard conditions. Our implementation of Data Security Compliance Monitor and Data Trust as a Service, along with three CSPs, each with ten different settings, has supported our proposition through the devised formula. Experimental outcomes show changes in the trustworthiness value with changes in compliance level, user feedback and auditor rating. CSPs with better compliance have better trustworthiness values. However, if the Accuracy of Compliance Measurement and Control Significance Factor are low the trustworthiness is also proportionately less. This creates a balance and realism in our calculations. This model is unique and will help in creating users' trust in cloud-based digital twins. [ABSTRACT FROM AUTHOR]

Introduction: Collaboration in teams composed of both humans and automation has an interdependent nature, which demands calibrated trust among all the team members. For building suitable autonomous teammates, we need to study how trust and trustworthiness function in such teams. In particular, automation occasionally fails to do its job, which leads to a decrease in a human's trust. Research has found interesting effects of such a reduction of trust on the human's trustworthiness, i.e., human characteristics that make them more or less reliable. This paper investigates how automation failure in a human-automation collaborative scenario affects the human's trust in the automation, as well as a human's trustworthiness towards the automation. Methods: We present a 2 χ 2 mixed design experiment in which the participants perform a simulated task in a 2D grid-world, collaborating with an automation in a "moving-out" scenario. During the experiment, we measure the participants' trustworthiness, trust, and liking regarding the automation, both subjectively and objectively. Results: Our results show that automation failure negatively affects the human's trustworthiness, as well as their trust in and liking of the automation. Discussion: Learning the effects of automation failure in trust and trustworthiness can contribute to a better understanding of the nature and dynamics of trust in these teams and improving human-automation teamwork. [ABSTRACT FROM AUTHOR]

This study investigates the significant impact of social media influencers on consumer purchasing behavior, focusing on the role of credibility and trustworthiness. A robust quantitative research approach was used to collect data from an online survey of 500 consumers who actively follow social media influencers. The results show a significant and positive relationship between social media influencers and consumer purchasing behavior. Notably, the credibility of these influencers and the nature of the products or services they promote emerge as important factors influencing consumer decisions. This study offers useful insights for marketers, emphasizing the importance of strategically selecting influencers and cultivating long-term relationships to increase credibility and trustworthiness. These findings provide practical advice for improving influencer marketing strategies and engaging with target audiences. [ABSTRACT FROM AUTHOR]

With the increasing interest in embedding digital devices in safety-critical cyber-physical systems (CPSs), such as industrial automation, aerospace, and automotive industries, attention has been directed toward proposing verifiable and reliable architectures. Prominent levels of formal verification and fault-tolerance are a requirement in dependable CPS systems to ensure system design meet the specifications and verify safety properties. In this paper, a novel formal verifiable and fault-tolerant hardware architecture uses the concepts of state machine, verification, and fault-tolerance as a foundation is developed. It is divided into four models: analysis model includes the functional requirements defined by the user, design model, the finite state machine is utilized to model the systems behavior which is tested by the NuSMV checker tool, implementation model simulates test cases on waveforms to validate the design against the requirements and verification model verifies functional and critical properties using mathematical formal linear time and computation tree logic to prove compliance with requirements and detect errors. The system uses temporal logic to formulate the required properties for a railway interlocking system (RIS) as a case study and symbolic model verifier (SMV) to demonstrate the correct execution. From the simulation results, the effectiveness of the architecture is proved for verifying critical properties and detecting design faults through majority voting circuits. The proposed architecture has been synthesized in the Altera FPGA programmable chip with logic elements 33%, 52% area overhead, and frequency as 100 MHz. The system does meet its reliability requirements with the lowest reliability 91.333687 x and failure rate 0.2 failure per hour at time 60 min. Finally, we think that adopting this architecture will enhance the trustworthiness and certification of CPS systems. [ABSTRACT FROM AUTHOR]

Imposter participants are an emerging concern in qualitative research due to the recent increase in online recruitment and virtual interviews. Unfortunately, there is limited guidance on how to address imposter participants in an equitable, trauma-informed way. To bridge this gap, we share imposter participant red flags, challenges, and a protocol that provides questions to ask and possible activities to engage in throughout the research process from conceptualization through findings dissemination. We ground this guidance in examples from our recent study with LGBTQ+ (e.g., lesbian, gay, bisexual, transgender, queer) survivors of sexual and intimate partner violence. [ABSTRACT FROM AUTHOR]

In 25 years, research on reputation-based online markets has produced robust evidence on the existence of the so-called reputation effect, that is the positive relation between online traders' reputations and these traders' market success in terms of sales and prices. However, there is an ongoing debate on what the size of the reputation effect means. We argue that the rate of truthful feedback that traders leave after completed transactions is negatively related to the size of the reputation effect. The higher the rate of truthful feedback, the quicker will untrustworthy traders be screened and disincentivized to enter the market. With mostly trustworthy traders entering the market, buyers will demand smaller price discounts from market entrants without a good reputation. We test this mechanism empirically in two laboratory experiments. In both experiments, we systematically vary the probability with which information about sellers' behavior in an economic trust game is recorded and shown to future interaction partners of these sellers. In the second experiment, we introduce competition among sellers by allowing buyers to choose one of two sellers in each interaction. We find that sellers give discounts to buyers to build or repair their reputation and that sellers who give discounts or have a good reputation are trusted more. However, we do not find support for our hypothesis that a higher feedback rate significantly decreases sellers' propensity to give discounts. We argue and show in exploratory analyses that this is likely due to the high level of unconditional trust buyers exhibit towards sellers without a reputation. Yet, seller competition increases the propensity to offer discounts among sellers without a reputation the most. [ABSTRACT FROM AUTHOR]

AbstractExposure to false information is becoming a common occurrence in our daily lives. New developments in artificial intelligence are now used to produce increasingly sophisticated multimedia false content, such as deepfakes, making false information even more challenging to detect and combat. This creates expansive opportunities to mislead individuals into believing fabricated claims and negatively influence their attitudes and behavior. Therefore, a better understanding of how individuals perceive such content and the variables related to the perceived trustworthiness of deepfakes is needed. In the present study, we developed and validated the Perceived Deepfake Trustworthiness Questionnaire (PDTQ) in English, Italian, and Slovene. This was done in three phases. First, we developed the initial pool of items by reviewing previous studies, generating items via interviews and surveys, and employing artificial intelligence. Second, we shortened and adapted the questionnaire according to experts’ evaluation of content validity and translated the questionnaire into Italian and Slovene. Lastly, we evaluated the psychometric characteristics via a cross-sectional study in three languages (N = 733). The exploratory factor analyses suggested a two-factor solution, with the first factor measuring the perceived trustworthiness of the content and the second measuring the perceived trustworthiness of the presentation. This factorial structure was replicated in confirmatory factor analyses. Moreover, our analyses provided support for PDTQ’s reliability, measurement invariance across all three languages, and its construct and incremental validity. As such, the PDTQ is a reliable, measurement invariant, and valid tool for comprehensive exploration of individuals’ perception of deepfake videos. [ABSTRACT FROM AUTHOR]

The use of AI algorithms in criminal trials has been the subject of very lively ethical and legal debates recently. While there are concerns over the lack of accuracy and the harmful biases that certain algorithms display, new algorithms seem more promising and might lead to more accurate legal decisions. Algorithms seem especially relevant for bail decisions, because such decisions involve statistical data to which human reasoners struggle to give adequate weight. While getting the right legal outcome is a strong desideratum of criminal trials, advocates of the relational theory of procedural justice give us good reason to think that fairness and perceived fairness of legal procedures have a value that is independent from the outcome. According to this literature, one key aspect of fairness is trustworthiness. In this paper, I argue that using certain algorithms to assist bail decisions could increase three different aspects of judges' trustworthiness: (1) actual trustworthiness, (2) rich trustworthiness, and (3) perceived trustworthiness. [ABSTRACT FROM AUTHOR]

Among other preventive measures, fact-checking is considered as one of the effective ways to combat fake news. Fact-checkers promote rationality and assist citizens in making informed decisions. However, fact-checkers' motives, methods and abilities are doubted and subjected to distrust. Lack of perceived trust is a major concern in the profession of fact-checking journalism. To build public trust, fact-checking organisations have been advised to be transparent about news production processes. Besides the trust mechanism, compliance to transparency is an important tenet of journalistic professionalism. This study seeks to analyse Indian fact-checkers' explanations of their methodology for fact-checking claims and commitments to ensuring methodological transparency. Qualitative content analysis of declarations published by seven Indian fact-checkers on their websites demonstrates that fact-checking is conducted in a systemic and transparent manner. Transparency norm seems to have emerged as a governing principle in the context of fact-checking journalism. The findings of this study have been interpreted in the view of literature on journalistic transparency. [ABSTRACT FROM AUTHOR]

Research Summary: Multimodal data, comprising interdependent unstructured text, image, and audio data that collectively characterize the same source, with video being a prominent example, offer a wealth of information for strategy researchers. We emphasize the theoretical importance of capturing the interdependencies between different modalities when evaluating multimodal data. To automate the analysis of video data, we introduce advanced deep machine learning and data fusion methods that comprehensively account for all intra‐ and inter‐modality interdependencies. Through an empirical demonstration focused on measuring the trustworthiness of grassroots sellers in live streaming commerce on Tik Tok, we highlight the crucial role of interpersonal interactions in the business success of microenterprises. We provide access to our data and algorithms to facilitate data fusion in strategy research that relies on multimodal data. Managerial Summary: Our study highlights the vital role of both verbal and nonverbal communication in attaining strategic objectives. Through the analysis of multimodal data—incorporating text, images, and audio—we demonstrate the essential nature of interpersonal interactions in bolstering trustworthiness, thus facilitating the success of microenterprises. Leveraging advanced machine learning techniques, such as data fusion for multimodal data and explainable artificial intelligence, we notably enhance predictive accuracy and theoretical interpretability in assessing trustworthiness. By bridging strategic research with cutting‐edge computational techniques, we provide practitioners with actionable strategies for enhancing communication effectiveness and fostering trust‐based relationships. Access our data and code for further exploration. [ABSTRACT FROM AUTHOR]

The enhancement of economic sustainability and the reduction of greenhouse gas (GHG) emissions are becoming more relevant in power system planning. Thus, renewable energy sources (RESs) have been widely used as clean energy for their lower generation costs and environmentally friendly characteristics. However, the strong random uncertainties from both the demand and generation sides make planning an economic, reliable, and ecological power system more complicated. Thus, this paper considers a variety of resources and technologies and presents a coordinated planning model including energy storage systems (ESSs) and grid network expansion, considering the trustworthiness of demand-side response (DR). First, the size of a single ESS was considered as its size has a close effect on maintenance costs and ultimately affects the total operating cost of the system. Second, it evaluates the influence of the trustworthiness of DR. Third, multiple resources and technologies were included in this high-penetration renewable energy integrated power system, such as ESSs, networks, DR technology, and GHG reduction technology. Finally, this model optimizes the decision variables such as the single size and location of ESSs and the operation parameters such as thermal generation costs, loss load costs, renewable energy curtailment costs, and GHG emission costs. Since the problem scale is very large not only due to the presence of various devices but also both binary and continuous variables considered simultaneously, we reformulate this model by decomposition. Then, we transform it into a master problem (MP) and a dual sub-problem (SP). Finally, the proposed method is applied to a modified IEEE 24-bus test system. The results show computational effectiveness and provide a helpful method in planning low-carbon electricity power systems. [ABSTRACT FROM AUTHOR]

Social capital theorists posit that association members are key agents for propagating norms of trust and trustworthiness from within associations toward the society as a whole. Nevertheless, others claim that social capital is primarily bonding, that is, it helps ingroup members better achieve internal goals, but little benefits or even costs carry over to the rest of society. We deploy experimental methods to probe into whether social capital in associations has a predominantly bridging or bonding nature. We compare members' behavior in anonymous Trust Games with behavior by a demographically comparable sample of non-members. We find that (a) Members are significantly more trusting and trustworthy than the general population both when interacting with fellow members and with people from the general population; (b) Members trust and repay trust from people from the general public nearly at the same level as they do with fellow members. Therefore, most of social capital existing within associations "bridges" over to the rest of society. We quantify 83% of additional trust, and 71% of additional trustworthiness existing in associations vis-à-vis society at large to be bridging and the remainder to be bonding. (c) Association members are no more optimistic or less accurate in predicting others' behavior than people from the general public. (d) Increased involvement in association activities is not correlated with increased pro-sociality. [ABSTRACT FROM AUTHOR]

Traditionally, knowledge and beliefs are attributed to agents. The article explores an alternative approach where knowledge is informed by data and belief comes from trust in, not necessarily reliable, data. At the core of the article is the modality “if one dataset is trusted, then another dataset informs a belief”. The main technical result is a sound and complete logical system capturing the properties of this modality and its connection with functional dependency between datasets. [ABSTRACT FROM AUTHOR]

What makes policy advice circulate more easily towards the apex of governments and shape the opinions of its users along the way? This article investigates the utilization of policy advice in ministerial cabinets, by focusing on the individuals appointed to work as closest aides of ministers: ministerial advisers. While we know quite a lot about the operation of policy advisory systems at the macro and meso levels, the micro-dispositions affecting the circulation and utilization of policy advice are under-researched topics. Building on recent research waves on policy advice and policy advisory systems, we expect that trust between ministerial advisers and their advice-suppliers is a pivotal driver of policy advice utilization. Based on an original survey conducted in the ministerial cabinets of French-speaking Belgium, this article provides evidence that ministerial advisers’ overall propensity to trust advice-suppliers tends to boost policy advice utilization. Yet, we show that the characteristics of advice suppliers also come into play in advisory relationships and that ministerial advisers have various needs in that regard, depending on them being high or low trust individuals. Our findings speak to the importance of paying attention to relational dynamics and informality in policy advising. [ABSTRACT FROM AUTHOR]

We examine whether social capital mitigates managerial opportunism around share repurchase announcements. We find that firms headquartered in high social capital states are associated with: (i) higher repurchase completion rates, and more so in environments where governance is weak and the potential for misleading investors is high, (ii) a smaller likelihood of information manipulation such as revealing bad news before repurchases, and (iii) lower completion rates when shares are less undervalued. By documenting that firms' external social environments help curb managerial opportunism, our study suggests that the location of headquarters facilitates trustworthiness and affects ethical considerations in corporate announcements. [ABSTRACT FROM AUTHOR]

Impressions of trustworthiness are formed quickly from faces. To what extent are these impressions shared among observers of the same or different races? Although high consensus of trustworthiness evaluation has been consistently reported, recent studies suggested substantial individual differences. For instance, negative implicit racial bias and low contact experience towards individuals of the other race have been shown to be related to low trustworthiness judgments for other-race faces. This pre-registered study further examined the effects of implicit social bias and experience on trustworthiness judgments of other-race faces. A relatively large sample of White (N = 338) and Black (N = 299) participants completed three tasks: a trustworthiness rating task of faces, a race implicit association test, and a questionnaire of experience. Each participant rated trustworthiness of 100 White faces and 100 Black faces. We found that the overall trustworthiness ratings for other-race faces were influenced by both implicit bias and experience with individuals of the other-race. Nonetheless, when comparing to the own-race baseline ratings, high correlations were observed for the relative differences in trustworthiness ratings of other-race faces for participants with varied levels of implicit bias and experience. These results suggest differential impact of social concepts (e.g., implicit bias, experience) vs. instinct (e.g., decision of approach-vs-avoid) on trustworthiness impressions, as revealed by overall vs. relative ratings on other-race faces. [ABSTRACT FROM AUTHOR]

The historically quantitative-dominated field of health sciences has increasingly embraced qualitative methods. However, calls for quantitative measures of rigor, such as Inter-coder Agreement (ICA), remain. The aim of this manuscript is to demystify ICA and provide practical guidance. I begin by describing considerations while planning for ICA, including differences between various ICA tests (i.e., percent agreement, Holsti Method, Cohen's kappa, Krippendorf's alpha, and Gwet's AC1 and AC2), setting the threshold of acceptability for your chosen test, deciding whether to use qualitative data analysis software, choosing the number of coders, selecting what data will be coded by more than one coder, developing a deductive codebook, creating a process for resolving coding disagreements, and establishing an audit trail for codebook changes. Next, I provide step-by-step guidance on an iterative process used for enacting ICA. Finally, I discuss the importance of reporting, emphasizing clarity, conciseness, completeness, and accuracy. [ABSTRACT FROM AUTHOR]

The 2015 Paris Agreement on climate change introduced Intended Nationally Determined Contributions (INDCs) into the process. INDCs share many features of pledges, i.e. public statements by parties in which they announce how they will behave in the social dilemma in the future. Prior evidence on how pledges enhance cooperation is inconclusive, however. We explore how differences in the information about pledgers' trustworthiness affect outcomes in a social dilemma that parallels climate change. In an online experiment, two participants interact with a randomly matched third player in a repeat maintenance game with a pledge stage. Treatments manipulate whether the third player is more or less trustworthy; and whether trustworthiness is observable. Disentangling composition and information effects, we find that only trustworthy pledgers can leverage the pledge stage for cooperation. This can explain evidence from social dilemmas such as international climate policy that reputational mechanisms in International Environmental Agreements are only effective when high-reputation countries are involved. • Prior evidence on how pledges enhance cooperation is inconclusive. • An experiment manipulates a pledger's trustworthiness and others' awareness thereof. • We find that only trustworthy pledgers can leverage the pledge stage for cooperation. • This can explain evidence from social dilemmas such as international climate policy. [ABSTRACT FROM AUTHOR]

With focus on the development and use of artificial intelligence (AI) systems in the digital health context, we consider the following questions: How does the European Union (EU) seek to facilitate the development and uptake of trustworthy AI systems through the AI Act? What does trustworthiness and trust mean in the AI Act, and how are they linked to some of the ongoing discussions of these terms in bioethics, law, and philosophy? What are the normative components of trustworthiness? And how do the requirements of the AI Act relate to these components? We first explain how the EU seeks to create an epistemic environment of trust through the AI Act to facilitate the development and uptake of trustworthy AI systems. The legislation establishes a governance regime that operates as a socio-epistemological infrastructure of trust which enables a performative framing of trust and trustworthiness. The degree of success that performative acts of trust and trustworthiness have achieved in realising the legislative goals may then be assessed in terms of statutorily defined proxies of trustworthiness. We show that to be trustworthy, these performative acts should be consistent with the ethical principles endorsed by the legislation; these principles are also manifested in at least four key features of the governance regime. However, specified proxies of trustworthiness are not expected to be adequate for applications of AI systems within a regulatory sandbox or in real-world testing. We explain why different proxies of trustworthiness for these applications may be regarded as 'special' trust domains and why the nature of trust should be understood as participatory. [ABSTRACT FROM AUTHOR]

Current surveys indicate limited public and individual trust in autonomous vehicles despite a long tradition to ensure their (technical) trustworthiness in informatics and systems engineering. To address this trust gap, this article explores the underlying reasons. The article elaborates on the gap between trust understood as a social phenomenon and, in contrast, the research tradition aimed at guaranteeing (technical) trustworthiness. It discusses to what extent those research traditions in the social sciences and humanities have been recognized and reflected in systems engineering research to date. Trust, according to the current state of research in the social sciences and humanities, heavily relies on individual assessments of an autonomous vehicle's abilities, benevolence and integrity. By contrast, technical trustworthiness is defined as the sum of intersubjective, measurable, technical parameters. They describe certain abilities or properties of a system, often according to respective technical standards and norms. This article places the "explainability" of autonomous systems in a bridging role. Explainability can help to conceptualize an integrative trust layer to communicate a system's abilities, benevolence and integrity. As such, explainability should respect the individual and situational needs of users, and should therefore be responsive. In conclusion, the results demonstrate that "learning from life" requires extensive interdisciplinary collaboration with neighboring research fields. This novel perspective on trustworthiness aligns existing research areas. It delves deeper into the conceptual "how", dives into the intricacies and showcases (missing) interconnectedness in the state of research. [ABSTRACT FROM AUTHOR]

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With machine learning (ML) becoming a popular tool across all domains, practitioners are in dire need of comprehensive reporting on the state-of-the-art. Benchmarks and open databases provide helpful insights for many tasks, however suffer from several phenomena: Firstly, they overly focus on prediction quality, which is problematic considering the demand for more sustainability in ML. Depending on the use case at hand, interested users might also face tight resource constraints and thus should be allowed to interact with reporting frameworks, in order to prioritize certain reported characteristics. Furthermore, as some practitioners might not yet be well-skilled in ML, it is important to convey information on a more abstract, comprehensible level. Usability and extendability are key for moving with the state-of-the-art and in order to be trustworthy, frameworks should explicitly address reproducibility. In this work, we analyze established reporting systems under consideration of the aforementioned issues. Afterwards, we propose STREP, our novel framework that aims at overcoming these shortcomings and paves the way towards more sustainable and trustworthy reporting. We use STREP's (publicly available) implementation to investigate various existing report databases. Our experimental results unveil the need for making reporting more resource-aware and demonstrate our framework's capabilities of overcoming current reporting limitations. With our work, we want to initiate a paradigm shift in reporting and help with making ML advances more considerate of sustainability and trustworthiness. [ABSTRACT FROM AUTHOR]

Examining and potentially adjusting one's cognitive processes in response to dissatisfaction with one's performance is a fundamental aspect of intelligence. Remarkably, such sophisticated abstract concepts necessary for achieving Artificial General Intelligence can be effectively incorporated into basic Machine Learning algorithms. In this study, we introduce a method for replicating self-awareness through a supervisory Artificial Neural Network (ANN), which monitors patterns in the activation functions of an underlying ANN to identify signs of substantial uncertainty within the underlying ANN and, consequently, the reliability of its predictions. The underlying ANN in this context is a Convolutional Neural Network (CNN) ensemble primarily utilized for tasks related to facial recognition and facial expression analysis. We evaluate the performance of the supervisory ANNs using various activation functions as they learn to gauge the dependability of predictions made by the Inception v3 CNN ensemble. To conduct computational experiments, we employ a facial data set that incorporates makeup and occlusion factors. These experiments are designed to mimic real-world conditions where the training data set exclusively consists of images without makeup or occlusion, while the test data set comprises images featuring makeup and occlusion. This partitioning ensures the model is tested under challenging out-of-training data distribution scenarios. [ABSTRACT FROM AUTHOR]

The effectiveness of vaccination programmes depends on high levels of public trust in political, scientific and health-related institutions, but public trust in vaccines can waver. This article explores aspects of public trust and mistrust on a web media platform about the MMR (measles-mumps-rubella) vaccine through the statements of a doctor and an anonymised 'anti-vaxxer'. Thematic analysis identifies commonalities and divergences in both perspectives. Both trust and mistrust of MMR vaccination are presented as moral, reasoned stances by their proponents; they are connected to the individual's experiences and situations, but are associated with very different trust attitudes to scientific and political institutions. Moreover, both the trustworthiness of the speakers themselves and the (un)trustworthiness of authorities are emphasised. Trust and mistrust are also thematised in relation to contextual matters such as the role of social media and the historical MMR controversy. Further research towards identifying common ground between trust positions is recommended. [ABSTRACT FROM AUTHOR]

Demographic data pertain to people's identities and behaviors. Analyses of demographic data are used to describe patterns and predict behaviors, to inform interface design, and even institutional decision-making processes. Demographic data thus need to be complete and correct to ensure they can be analyzed in ways that reflect reality. This study consists of interviews with 40 people in STEM and addresses how causes of relational (dis)trust in demographic data collection contribute to pervasive problems of missing and incorrect responses and disobliging responses (e.g., non-disclosure, false responses, attrition, and hesitancy to use services). The findings then guide a preliminary set of recommendations for cultivating trustworthiness based on recent developments in trust theory and designing for responsive and trustworthy systems. Specifically, we explore how demographic questionnaire design (e.g., item construction and instructions) can communicate necessary reassurances and transparency for users. The ongoing research provides interview-based recommendations for improving the quality and completeness of demographic data collection. This research adds to other recommendations on improving demographic questionnaires. [ABSTRACT FROM AUTHOR]

When listeners hear a voice, they rapidly form a complex first impression of who the person behind that voice might be. We characterize how these multivariate first impressions from voices emerge over time across different levels of abstraction using electroencephalography and representational similarity analysis. We find that for eight perceived physical (gender, age, and health), trait (attractiveness, dominance, and trustworthiness), and social characteristics (educatedness and professionalism), representations emerge early (~80 ms after stimulus onset), with voice acoustics contributing to those representations between ~100 ms and 400 ms. While impressions of person characteristics are highly correlated, we can find evidence for highly abstracted, independent representations of individual person characteristics. These abstracted representationse merge gradually over time. That is, representations of physical characteristics (age, gender) arise early (from ~120 ms), while representations of some trait and social characteristics emerge later (~360 ms onward). The findings align with recent theoretical models and shed light on the computations underpinning person perception from voices. [ABSTRACT FROM AUTHOR]

Self-study researchers have periodically considered the essence of self-study, its objectives, its applications, and potential future trajectories. To address this imperative, our study undertakes a content analysis of the initial 19 volumes of Studying Teacher Education (STE), the flagship journal of self-study research. We focus specifically on the methodological dimensions of the scholarship featured in the journal, encompassing the spectrum of self-study methodologies employed, data collection techniques utilized, and measures of trustworthiness adopted. Through our analysis, we identify prevalent trends within each thematic domain, alongside an evolving consensus over time regarding the characteristics of high-quality self-study research. Nonetheless, we also discern persistent challenges concerning the lack of definitional precision and consistent implementation across diverse methodologies, data collection approaches, and trustworthiness measures. Echoing previous scholarship, we advocate for the establishment of a shared lexicon within the self-study community and the cultivation of explicit understandings regarding the diverse modalities of its application. Furthermore, drawing from our analysis, we introduce a typology delineating three distinct epochs of self-study research within the journal’s history while also envisioning the emergence of a fourth era poised to both reaffirm self-study’s foundational role as a form of counter-scholarship and chart innovative pathways forward. [ABSTRACT FROM AUTHOR]

Facial features are important sources of information about perceived trustworthiness. Masks and protective clothing diminish the visibility of facial cues by either partially concealing the mouth and nose or covering the entire face. During the pandemic, the use of personal protective equipment affected and redefined who trusts whom in society. This study used the classical investment game of interpersonal trust with Chinese participants to explore the impact of occlusion on interpersonal trust. Faces with moderate initial trustworthiness were occluded by a mask or protective clothing in Experiment 1 and were digitally occluded by a square in Experiment 2, and faces with three levels of initial trustworthiness were occluded by a mask in Experiment 3. Results showed that both undergraduates (Experiment 1a) and non-student adults (Experiment 1b) perceived the faces with protective clothing as more trustworthy than faces wearing standard masks and faces not wearing masks. Faces with the top halves showing were perceived as trustworthy as full faces, while faces with the bottom halves showing were perceived as less trustworthy. The effect of masks is weak and complex. Masks reduced participants' trust in faces with high initial trustworthiness, had no effect on faces with low and moderate initial trustworthiness, and only slightly increased the trust of undergraduates in faces with moderate initial trustworthiness. Our findings indicate that the lack of information caused by occlusion and the social significance associated with occlusion collectively affect people's trust behavior in Chinese society. We believe the findings of this study will be useful in elucidating the effects of personal protective equipment usage on perceptions of trustworthiness. [ABSTRACT FROM AUTHOR]

Trustworthiness is the most significant predictor of trust and has a significant impact on people's levels of trust. Most trustworthiness-related research is empirical, and while it has a long history, it is challenging for academics to get insights that are applicable to their fields of study and to successfully transfer fragmented results into practice. In order to grasp their dynamic development processes through the mapping of network knowledge graphs, this paper is based on the Web of Science database and uses CiteSpace (6.2.R4) software to compile and visualize the 1,463 publications on trustworthy studies over the past 10 years. This paper aims to provide valuable references to theoretical research and the practice of Trustworthiness. The findings demonstrate that: over the past 10 years, trustworthiness-related research has generally increased in volume; trustworthiness research is concentrated in industrialized Europe and America, with American research findings having a bigger global impact; The University of California System, Harvard University, and Yale University are among the high-production institutions; the leading figures are represented by Alexander Todorov, Marco Brambilla, Bastian Jaeger, and others; the core authors are distinguished university scholars; however, the level of cooperation of the core author needs to be improved. The primary journal for publishing research on trustworthiness is the Journal of Personality and Social Psychology and Biology Letters. In addition, the study focuses on three distinct domains, involving social perception, facial clues, and artificial intelligence. [ABSTRACT FROM AUTHOR]

When laypersons are presented with scientific information which seeks to modify their way of life, they are expected to believe, suspend belief, or reject it. Second-order assessment of scientific experts helps laypersons to make an informed decision in such situations. This is an assessment of the trustworthiness of the person making the scientific claim. In this paper I challenge the optimistic view of Anderson (2011), regarding the ease with which laypersons can perform second-order assessment of experts, by pointing out some of the obstacles that may prevent laypersons from arriving at an informed decision through this means. By showing that laypersons cannot easily perform second-order assessment of experts, I make a case for sharing epistemic burdens in science communication by using Lackey's (2006) concept of dualism in the epistemology of testimony and Irzik and Kurtulmus' (2019) work on public epistemic trust in science, as a guide. I invite experts to bear a greater share of the epistemic burden when communicating with laypersons because of their privileged epistemic condition vis-à-vis laypersons. [ABSTRACT FROM AUTHOR]

The study aimed to answer what explains the perceived trustworthiness of female civil servants in three South Asian countries using a country-representative survey (n = 6240; 2,740 in Bangladesh; 2,256 in Nepal and 1,244 in Sri Lanka). The study finds that efficiency, level of corruption, life satisfaction, and generalized trust seem to have varied levels of impact in determining the trustworthiness of female civil servants. Perceived efficiency level is closely associated with the degree of trust in female civil servants in Bangladesh and Nepal, whereas corruption seems to have a significant influence in Sri Lanka. Life satisfaction affects all three countries, gender affects Bangladesh and Sri Lanka, whereas education has a positive association in Nepal and Sri Lanka. [ABSTRACT FROM AUTHOR]

During social interactions, people decide whether to trust an actor based on their punitive behaviour. Several empirical studies have indicated that punishment intensity impacts observer trust, yet the underlying mechanism remains to be elucidated. This study included 242 junior high school students and was conducted to investigate the relationship between teachers' punishment intensity and levels of student bystander trust. Additionally, the mediating role of trustworthiness and the moderating role of group relationships were explored. The results showed that the relationship between punishment intensity and observer trust follows an inverted U-shaped pattern. In addition, mild punishment boosts observer trust by improving perceived trustworthiness (ability and integrity) compared to no punishment, while harsh punishment reduces observer trust more than mild punishment by diminishing perceived trustworthiness (ability, benevolence, and integrity). More importantly, group relationships positively moderate the relationship between punishment intensity and observer trust. Specifically, compared to mild or no punishment, harsh punishment decreases trustworthiness (ability, benevolence, and integrity) in close teacher–student relationships but has less impact on neutral relationships. The above findings demonstrate that guiding educators in developing appropriate disciplinary concepts contributes to enhancing student observer trust. [ABSTRACT FROM AUTHOR]

Artificial intelligence-based algorithms are widely adopted in critical applications such as healthcare and autonomous vehicles. Mitigating the security and privacy issues of AI models, and enhancing their trustworthiness have become of paramount importance. We present a detailed investigation of existing security, privacy, and defense techniques and strategies to make machine learning more secure and trustworthy. We focus on the new paradigm of machine learning called federated learning, where one aims to develop machine learning models involving different partners (data sources) that do not need to share data and information with each other. In particular, we discuss how federated learning bridges security and privacy, how it guarantees privacy requirements of AI applications, and then highlight challenges that need to be addressed in the future. Finally, after having surveyed the high-level concepts of trustworthy AI and its different components and identifying present research trends addressing security, privacy, and trustworthiness separately, we discuss possible interconnections and dependencies between these three fields. All in all, we provide some insight to explain how AI researchers should focus on building a unified solution combining security, privacy, and trustworthy AI in the future. [ABSTRACT FROM AUTHOR]

Many recent AI policies have been structured under labels that follow a particular trend: national or international guidelines, policies or regulations, such as the EU’s and USA’s ‘Trustworthy AI’ and China’s and India’s adoption of ‘Responsible AI’, use a label that follows the recipe of [agentially loaded notion + ‘AI’]. A result of this branding, even if implicit, is to encourage the application by laypeople of these agentially loaded notions to the AI technologies themselves. Yet, these notions are appropriate only when applied to agents, which current AI technologies are not; and the concern is that this misapplication creates an incentive to inappropriately attribute trustworthiness or responsibility to AI technologies. We endeavour to show that we have good reason to avoid any general AI policy that uses agentially loaded labelling. We suggest labelling these policies not in terms of some qualification of AI, but rather in terms of our approach to the technology and its wider development and use context – focusing on being trustworthy and responsible about AI, rather than on trustworthy or responsible AI. [ABSTRACT FROM AUTHOR]

Many policies and ethical guidelines recommend developing “trustworthy AI”. We argue that developing morally trustworthy AI is not only unethical, as it promotes trust in an entity that cannot be trustworthy, but it is also unnecessary for optimal calibration. Instead, we show that reliability, exclusive of moral trust, entails the appropriate normative constraints that enable optimal calibration and mitigate the vulnerability that arises in high-stakes hybrid decision-making environments, without also demanding, as moral trust would, the anthropomorphization of AI and thus epistemically dubious behavior. The normative demands of reliability for inter-agential action are argued to be met by an analogue to procedural metacognitive competence (i.e., the ability to evaluate the quality of one’s own informational states to regulate subsequent action). Drawing on recent empirical findings that suggest providing reliability scores (e.g., F1-scores) to human decision-makers improves calibration in the AI system, we argue that reliability scores provide a good index of competence and enable humans to determine how much they wish to rely on the system. [ABSTRACT FROM AUTHOR]

Postulating a negative bias towards social ambiguity, we conducted cross‐cultural online research to assess whether categorical discrepancies in the perception of androgynous faces were associated with the uncanny feeling and inferences of different morality. Across four studies, we found that androgynous faces were harder to classify into a binary sex category than sex‐typical faces, but this difficulty did not influence social judgements of androgynous targets in a negative fashion. In Study 1 (Spanish‐speaking sample, N = 76), we found that androgynous faces were rated as more trustworthy, less creepy, and less morally different than sex‐typical faces. Study 2 replicated most of the findings from Study 1 in an Italian sample (N = 45). Positive bias towards androgyny was not replicated with a different set of stimuli featuring faces of diverse ethnic backgrounds (Study 3, Spanish‐speaking sample, N = 140). However, results revealed a main effect of ethnicity in participants' responses. When controlling for the effect of morphing procedures in stimuli selection, an overall positive bias towards androgynous targets arose, especially when compared to masculine targets (Study 4, Spanish‐speaking sample, N = 85). These findings suggest that, at least in certain conditions, a positive social bias towards androgynous faces may emerge that does not depend on categorical uncertainty and facial attractiveness. [ABSTRACT FROM AUTHOR]

Researchers play an essential, and indeed powerful, role in honouring and empowering the voices of people from marginalised communities. This chapter seeks to step beyond the already comprehensive ethical and methodological literature on 'doing' research by offering a reflection on the less articulated, but no less substantiative, aspects of conducting qualitative research with those for whom that which is most important, as the writer Audrey Lorde suggests, must be spoken. The delicate dance of interest and objectivity, a tolerance of not knowing and uncertainty and the willingness to hold competing truths with equal reverence are discussed and illuminated with examples from my own research with young people with experiences of mental health difficulties. This chapter is offered with the intention of foregrounding some of the more tacit, but no less bruising, aspects of the research interplay. Equally, it is offered in the hope that, in bringing into the open our limitations and vulnerabilities as researchers, we might be better positioned to understand, indeed honour, that which is most important for those in distress. [ABSTRACT FROM AUTHOR]