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.

UNASSIGNED: Survivors 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.\"
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

Increasing integrity concerns in medical research have prompted the development of tools to detect untrustworthy studies. Existing tools primarily assess published aggregate data (AD), though scrutiny of individual participant data (IPD) is often required to detect trustworthiness issues. Thus, we developed the IPD Integrity Tool for detecting integrity issues in randomised trials with IPD available. This manuscript describes the development of this tool. We conducted a literature review to collate and map existing integrity items. These were discussed with an expert advisory group; agreed items were included in a standardised tool and automated where possible. We piloted this tool in two IPD meta-analyses (including 116 trials) and conducted preliminary validation checks on 13 datasets with and without known integrity issues. We identified 120 integrity items: 54 could be conducted using AD, 48 required IPD, and 18 were possible with AD, but more comprehensive with IPD. An initial reduced tool was developed through consensus involving 13 advisors, featuring 11 AD items across four domains, and 12 IPD items across eight domains. The tool was iteratively refined throughout piloting and validation. All studies with known integrity issues were accurately identified during validation. The final tool includes seven AD domains with 13 items and eight IPD domains with 18 items. The quality of evidence informing healthcare relies on trustworthy data. We describe the development of a tool to enable researchers, editors, and others to detect integrity issues using IPD. Detailed instructions for its application are published as a complementary manuscript in this issue.

Increasing concerns about the trustworthiness of research have prompted calls to scrutinise studies\' Individual Participant Data (IPD), but guidance on how to do this was lacking. To address this, we developed the IPD Integrity Tool to screen randomised controlled trials (RCTs) for integrity issues. Development of the tool involved a literature review, consultation with an expert advisory group, piloting on two IPD meta-analyses (including 73 trials with IPD), preliminary validation on 13 datasets with and without known integrity issues, and evaluation to inform iterative refinements. The IPD Integrity Tool comprises 31 items (13 study-level, 18 IPD-specific). IPD-specific items are automated where possible, and are grouped into eight domains, including unusual data patterns, baseline characteristics, correlations, date violations, patterns of allocation, internal and external inconsistencies, and plausibility of data. Users rate each item as having either no issues, some/minor issue(s), or many/major issue(s) according to decision rules, and justification for each rating is recorded. Overall, the tool guides decision-making by determining whether a trial has no concerns, some concerns requiring further information, or major concerns warranting exclusion from evidence synthesis or publication. In our preliminary validation checks, the tool accurately identified all five studies with known integrity issues. The IPD Integrity Tool enables users to assess the integrity of RCTs via examination of IPD. The tool may be applied by evidence synthesists, editors and others to determine whether an RCT should be considered sufficiently trustworthy to contribute to the evidence base that informs policy and practice.

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With the ever-increasing number of artificial intelligence (AI) systems, mitigating risks associated with their use has become one of the most urgent scientific and societal issues. To this end, the European Union passed the EU AI Act, proposing solution strategies that can be summarized under the umbrella term trustworthiness. In anti-cancer drug sensitivity prediction, machine learning (ML) methods are developed for application in medical decision support systems, which require an extraordinary level of trustworthiness. This review offers an overview of the ML landscape of methods for anti-cancer drug sensitivity prediction, including a brief introduction to the four major ML realms (supervised, unsupervised, semi-supervised, and reinforcement learning). In particular, we address the question to what extent trustworthiness-related properties, more specifically, interpretability and reliability, have been incorporated into anti-cancer drug sensitivity prediction methods over the previous decade. In total, we analyzed 36 papers with approaches for anti-cancer drug sensitivity prediction. Our results indicate that the need for reliability has hardly been addressed so far. Interpretability, on the other hand, has often been considered for model development. However, the concept is rather used intuitively, lacking clear definitions. Thus, we propose an easily extensible taxonomy for interpretability, unifying all prevalent connotations explicitly or implicitly used within the field.

UNASSIGNED: Voice as a health biomarker using artificial intelligence (AI) is gaining momentum in research. The noninvasiveness of voice data collection through accessible technology (such as smartphones, telehealth, and ambient recordings) or within clinical contexts means voice AI may help address health disparities and promote the inclusion of marginalized communities. However, the development of AI-ready voice datasets free from bias and discrimination is a complex task. The objective of this study is to better understand the perspectives of engaged and interested stakeholders regarding ethical and trustworthy voice AI, to inform both further ethical inquiry and technology innovation.
UNASSIGNED: A questionnaire was administered to voice AI experts, clinicians, scholars, patients, trainees, and policy-makers who participated at the 2023 Voice AI Symposium organized by the Bridge2AI-Voice AI Consortium. The survey used a mix of Likert scale, ranking and open-ended questions. A total of 27 stakeholders participated in the study.
UNASSIGNED: The main results of the study are the identification of priorities in terms of ethical issues, an initial definition of ethically sourced data for voice AI, insights into the use of synthetic voice data, and proposals for acting on the trustworthiness of voice AI. The study shows a diversity of perspectives and adds nuance to the planning and development of ethical and trustworthy voice AI.
UNASSIGNED: This study represents the first stakeholder survey related to voice as a biomarker of health published to date. This study sheds light on the critical importance of ethics and trustworthiness in the development of voice AI technologies for health applications.

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.

Since its recognition as a plausible direction to assure food safety, food safety culture research has evolved with several commercial and scientific assessment tools developed to evaluate the food safety culture in food businesses. However, existing research does not specify the validity and reliability checks required to demonstrate rigor in the tool development process and there is no unified methodology to confirm robustness of the tools to ensure trustworthiness and usefulness of findings and inferences generated. The purpose of the study was to develop a method to evaluate food safety culture assessment tools and to assess the reliability and validity of existing food safety culture assessment tools using the developed method. Eleven elements were found to be key in validating food safety culture assessment tools. Of the eight tools assessed, only one tool (CT2) was validated on each of the elements. The depth of validation strategies differed for each tool. Three out of the five commercial tools published peer reviewed publications that demonstrated the validation checks that were done. Face validation, and pilot testing were evident and appeared to be done the most. Whilst content, ecological, and cultural validity were the least validated for scientific tools, factor analysis and reliability checks were the least evaluated for commercial tools. None of the tools were assessed for postdictive validity, concurrent validity and the correlation coefficient relating to construct validity. Having an established science-based approach is key as it provides a way to determine the trustworthiness of established assessment tools against accepted methods.

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.