Trust Gap Research Articles

Race & Political Trust: Justice as a Unifying Influence on Political Trust

Abstract Americans’ trust in government is lower than ever. However, while all groups have seen a decline in trust since the 1960s, the gap in trust between racial and ethnic minorities and Whites in this period has varied not only in size but also in direction. At times, racial and ethnic minorities have actually had higher rates of trust than Whites, contradicting the broad assumptions in research about race and political trust. Explanations of the causes of trust in government that emphasize institutional experience and early socialization would not predict this outcome. We propose that an underutilized component in the study of race and political trust is perceived justice. On one hand, racial and ethnic minorities’ sensitivity to institutional injustice often leads to lower rates of trust. On the other hand, when racial and ethnic minorities perceive there are greater opportunities for racial progress, which signal that widespread harm can be repaired, their political trust tends to increase, sometimes to levels that exceed those for Whites. The interplay between political realities that shape perceived justice as well as political hope for racial progress likely creates the variable longitudinal patterns of racial and ethnic differences in trust.

Don't Tell Me What I Don't Want to Hear! Politicization and Ideological Conflict Explain Why Citizens Have Lower Trust in Climate Scientists and Economists Than in Other Natural Scientists

Studies suggest that citizens have higher trust in some groups of scientists than in others. However, we still know little about the causes of these trust gaps. The current study fills this knowledge gap by examining Norwegian citizens' trust in climate scientists, economists, and so‐called “less politicized natural scientists.” I argue that trust in climate scientists and economists is lower than trust in less politicized natural scientists because the former fields are politicized, while the latter are not. Politicization strengthens ideological conflicts between citizens' ideology and research produced by climate scientists and economists, which leads to lower trust in these groups of scientists. I test this argument by running regression analyses on data from a representative survey of the Norwegian population. The results support the argument: Citizens have significantly higher trust in less politicized natural scientists than in both climate scientists and economists, and these differences can be explained by ideological biases in trust. Citizens with a proeconomic growth ideology have significantly lower trust in climate scientists than in less politicized natural scientists, and citizens with a left‐wing economic ideology have significantly lower trust in economists than in less politicized natural scientists.

Happy Winners, Sore Partisans? Political Trust, Partisanship, and the Populist Assault on Electoral Integrity in Mexico

Winning elections usually make partisan voters more politically satisfied and confident. However, if they voted for a president that actively undermines the legitimacy of democratic institutions, they will be compelled to accommodate their views and update their judgment on a selective basis. They will support the regime's performance and yet distrust the institutions denounced by the government. This claim is tested using data from a representative survey conducted in Mexico. In this country, the president is a populist leader who consistently denounces all constraints on the executive. In particular, the president frequently undermines the institutions safeguarding free and fair elections. The analysis reveals that the gap in political trust reflects the opposite reactions from partisan winners and losers to the executive's antagonizing behavior. Voters supporting the winning party are more satisfied with democracy. However, they are less likely to trust the integrity of elections than the partisan losers.

Leveraging Public-Private Partnerships with Community to Increase Health Equity: Building Sustainable Models Guided by Data, Policy, and Practice.

Leveraging Public-Private Partnerships with Community to Increase Health Equity:Building Sustainable Models Guided by Data, Policy, and Practice Roslyn Holliday Moore, MS (bio), Juliet Bui, MPA, MSW (bio), Rachel R. Ouellette, PhD (bio), Bridget Kerner, MS (bio), Michael N. Awad, PhD (bio), Alexis F. Leal, MPH, CPHQ (bio), Yanique Edmond, PhD, MPA (bio), Stacey L. Williams (bio), and Derrick M. Gordon, PhD (bio) The nation's quest to dismantle persistent health disparities is marked by policy innovations to promote equitable health. Approaches to interpreting, translating, and monitoring health-related policies can activate key multisectoral partnerships between the federal, state, local governments, and private organizations to foster and sustain community-centered health care and health equity. In these partnerships, the federal government provides funding, strategic direction, logistical support, and encourages further collaborations based on the synthesis of national and local data as part of a singular strategic vision. Through these strategic visions, state and local governments allocate resources, disseminate best practices, and implement localized policies to amplify opportunities for facilitating equitable health outcomes and engaging community-based organizations. Community-based organizations, as trusted messengers, are essential partners for delivering culturally and linguistically appropriate health care. For these layered partnerships to be successful, they must be collaborative, bidirectional, and collectively focused on improving and achieving equitable health care access, use, and outcomes. [End Page 138] Racial and ethnic minority, low-income, and other socially vulnerable populations have experienced long-standing health disparities in the access, use, and outcomes of health care during and prior to the COVID-19 pandemic. Data from the coronavirus disease 2019 (COVID-19) pandemic have highlighted higher exposure, infection, hospitalization, and death rates among racial and ethnic minority populations in comparison with the non-Hispanic White population.1–7 This pattern has been seen with other outbreaks, including the H1N1 pandemic,8 the HIV/AIDS epidemic,9 and even the seasonal flu outbreaks.10 These patterns are due in part to racial and ethnic minority and poor populations being at higher risk for pre-existing health conditions (including diabetes, hypertension, obesity, and coronary heart disease) that increase their likelihood of contracting severe forms of COVID-19.11–13 Consistent inequitable outcomes across health conditions and outbreaks indicate root causes that extend beyond biological issues to include social, systemic, and structural factors.14 Influencers of health, including policies and social determinants of health, provide a lens for understanding patterns of inequity across health outcomes. Two key influencers in the context of COVID-19 are social determinants of health and social vulnerability, which are respectively defined as "the conditions in the places where people live, learn, work, and play that affect a wide range of health risks and outcomes."15[unpaginated] and "the characteristics of a person or community that affect their capacity to anticipate, confront, repair, and recover from the effects of a disaster," such as socioeconomic status and minority status.16[p.1] Understanding their influence can inform policy direction. For instance, systemic inequities in access to health insurance and high-quality care make it difficult to connect racial and ethnic minority and low-income populations with COVID-19 services, likely contributing to sometimes lower rates of testing and vaccination.17–19 Racial and ethnic minority and low-income individuals have also experienced a long-standing history of bias and discrimination in medical settings.20–21 Specific to the pandemic, data revealed that providers were less likely to offer COVID-19 testing to Black patients experiencing the same symptoms as White patients, contributing to medical distrust and vaccine hesitancy.22 Black and Hispanic/Latino individuals have indicated, however, that they would be more likely to consider getting the vaccine if they heard information or experiences from same-race medical professionals and community members, than if they heard it from others.23 These findings suggest that disparate outcomes and community context cannot be addressed without an equity lens or without policies that promote partnerships across sectors. Specifically, pandemic response efforts that integrate alliances with entities that are based in or led by racial and ethnic minority and low-income communities can help bridge access and trust gaps while improving engagement and participation in mitigation efforts. We begin this...

Trustworthy: How the Smartest Brands Beat Cynicism and Bridge the Trust Gap: Margot Bloomstein: [Book Review

In the Introduction to this book, the author presents a picture of rampant mistrust of our organizations and institutions: Consumers, citizens, and corporate buyers are feeling a sweeping, protective skepticism that undermines the delivery of information, products, and services in every sector of the economy. How can an organization communicate effectively in this context? Through pithy case studies and interviews with communicators in those organizations, the author presents a blueprint for clear, effective communication that evokes trust, educates the audience, and supports the audience in effective decision-making. Case studies include private and public companies, individual communicators who are supporting a target constituency, and government agencies. The value of this book is in its presentation of real-world communication, a real world in which both internal and external circumstances change, a real world in which organizations must decide who they are and who they want to be to their audience, a real world in which determining your organization’s voice and tone may not be sufficient. The book is a notable addition to texts that support professional and business communication and content strategy, and is a valuable supplemental text for courses in those areas. The book includes resources for further reading, identities of individuals interviewed for the book, endnotes, and an index.

An authenticated and secure accounting system for international emissions trading

ABSTRACT Expanding the international emissions trading system is crucial to filling the existing mitigation gap for the 2°C climate target. Trustworthy emissions accounting is the cornerstone of such a system encompassing different jurisdictions. However, traditional emissions measuring, reporting, and verification, as well as data disclosure practices that support data authenticity in a regional emissions trading system, might not be compatible with data transparency requirements in a future international system that covers countries with gaps in governance and social trust. This transparency requirement can cause severe data security concerns. In this study, we propose a cryptographic framework for an authenticated and secure emissions accounting system that can resolve this dilemma. We demonstrate that integrating a sequence of cryptographic protocols can preserve data authenticity and security for a stylized multi-country emissions trading system. We call for more research to promote joint work by policy researchers and computer scientists to strengthen collaboration in future international climate governance. Key policy insights Data authenticity and security concerns are key issues that have hindered international cooperation in mitigating climate change. Modern cryptography can help preserve both data authenticity and security in a multi-country emissions trading setting. More research is needed to promote joint work by policy researchers and computer scientists on international climate governance.

Bridging the Trust Gap in Influencer Marketing: Ways to Sustain Consumers’ Trust and Assuage Their Distrust in the Social Media Influencer Landscape

While consumers’ trust in social media influencers (SMIs) has spurred the growth of influencer marketing, consumers have come to distrust what these SMIs say recently. Despite this trend reversal that the industry is undergoing, academic efforts to investigate whether, and if so, why, consumers experience such attitudinal ambivalence in trust and distrust in the SMI landscape is notably lacking. Building upon the concept of attitudinal ambivalence between trust and distrust, this study addresses this gap in the literature. The study used a mixed-method approach. In Study 1, we conducted an exploratory, qualitative study using two focus group interviews. During the interviews, participants were asked to discuss the open-ended questions freely, which included such questions as “What do you think about your choice of SMI in terms of trustworthiness?” and “If you trust (or distrust, or feel ambivalent toward) SMIs, why?” Based upon the findings from Study 1, Study 2 developed and tested empirically the conceptual framework that describes the antecedents and consequences of consumers’ ambivalence within the SMI context: (1) whether SMIs’ personality traits (similarity and attractiveness) and content attributes (visual appeal and informativeness) promote consumers’ trust in the SMIs; (2) whether perceived ad clutter triggers their distrust of the SMIs’ branded content, and (3) whether the attitudinal ambivalence in trust and distrust affect their behavior (i.e., behavioral intention to imitate the SMIs’ product and brand choices) independently as well as interdependently. Except for attractiveness and visual appeal, the online survey consumer data supported the proposed relations strongly, and the results provide meaningful implications for both theory and practice.

Zero Trust Architecture: Trend and Impact on Information Security

Traditional-based security models are a threat to information security; they have been regarded as weak and ineffective to meet the dynamics of information system trust. An emerging framework, Zero Trust Architecture (ZTA) seeks to close the trust gap in information security through enforcing policies based on identity and continuous authentication and verification. This framework is built on several trust nodes and logical components that attempt to close the trust gap that exists in an information system. The adoption of this framework is still in its teething stage which is a result of several misleading deductions and assumptions. We attempt to explore the intricacies in the framework and close the existing knowledge gap. we surveyed the literature on ZTA and provided a foundational discussion on its implementation and effectiveness from prior studies. while we do not critique other models, this paper studied the strength and variables of the zero-trust security architecture and attempt to provide an overview of the model and close the knowledge gap on the effectiveness of adopting a Zero trust philosophy.

Why is trust lower in Quebec? A cultural explanation.

In this article, we provide a cultural explanation of a long-standing trust puzzle in Canada-Quebecers trust much less than their fellow Canadians. Specifically, we develop a novel approach to empirically assess the historical influence of the Catholic Church, using the Quiet Revolution (a period of abrupt modernization in Quebec) as a natural experiment. We find that older cohorts socialized prior to the Quiet Revolution are significantly less trusting-a distinctive trend that is most pronounced among Catholics. Conversely, in the rest of Canada older cohorts are more trusting, following the trend commonly found in other countries. Furthermore, measures of both religious beliefs and modernization account for a large part of the birth cohort trust gap in Quebec. The findings suggest that low trust in Quebec is rooted in the province's Catholic cultural heritage, but that the legacy of the Quiet Revolution is gradually changing the trust culture.

A Quantitative Comparative Study of Data-oriented Trust Management Schemes in Internet of Things

In the Internet of Things (IoT) paradigm, all entities in the IoT network, whether home users or industrial things, receive data from other things to make decisions. However, in the decentralized, heterogeneous, and rapidly changing IoT network with billions of devices, deciding about where to get the services or information from is critical, especially because malicious entities can exist in such an unmanaged network. Security provisioning alone cannot solve the issue of service quality or reliability. One way to elevate security and reliability in the IoT network is to bridge the gap of trust between objects, and also between humans and objects, while taking into account the IoT network characteristics. Therefore, a proper trust management system must be established on top of the IoT network service architecture. Trust is related to the manner expected from objects in providing services and recommendations. Recommendations are the basis of decision making in every trust management system. Since trust management ideas in the IoT are still immature, the purpose of this article is to survey, analyze, and compare the approaches that have been taken in building trust management systems for the IoT. We break down the features of such systems by analysis and also do quantitative comparisons by simulation. This article is organized into two main parts. First, studies and approaches in this field are compared from four perspectives: (1) trust computation method, (2) resistance to attacks (3) adherence to the limitations of IoT networks and devices, and (4) performance of the trust management scheme. The second part is quantitative and simulates four major methods in this field and measures their performance. We also make extensive analytical comparisons to demonstrate the similarities and discrepancies of current IoT trust management schemes and extract the essence of a resilient trust management framework.

A deep learning method for automatic SMS spam classification: Performance of learning algorithms on indigenous dataset

AbstractSMS, one of the most popular and fast‐growing GSM value‐added services worldwide, has attracted unwanted SMS, also known as SMS spam. The effects of SMS spam are significant as it affects both the users and the service providers, causing a massive gap in trust among both parties. This article presents a deep learning model based on BiLSTM. Further, it compares our results with some of the states of the art machine learning (ML) algorithm on two datasets: our newly collected dataset and the popular UCI SMS dataset. This study aims to evaluate the performance of diverse learning models and compare the result of the new dataset expanded (ExAIS_SMS) using the following metrics the true positive (TP), false positive (FP), F‐measure, recall, precision, and overall accuracy. The average accuracy for the BiLSTSM model achieved moderately improved results compared to some of the ML classifiers. The experimental results achieved significant improvement from the ground truth results after effective fine‐tuning of some of the parameters. The BiLSTM model using the ExAIS_SMS dataset attained an accuracy of 93.4% and 98.6% for UCI datasets. Further comparison of the two datasets on the state‐of‐the‐art ML classifiers gave an accuracy of Naive Bayes, BayesNet, SOM, decision tree, C4.5, J48 is 89.64%, 91.11%, 88.24%, 75.76%, 80.24%, and 79.2% respectively for ExAIS_SMS datasets. In conclusion, our proposed BiLSTM model showed significant improvement over traditional ML classifiers. To further validate the robustness of our model, we applied the UCI datasets, and our results showed optimal performance while classifying SMS spam messages based on some metrics: accuracy, precision, recall, and F‐measure.

Promise of AI in DeFi, a Systematic Review

Decentralized Finance (DeFi) is an emerging and revolutionizing field with notable uncertainties of reliability to be used on a mass scale. On the other hand, Artificial Intelligence (AI) has proved to be a crucial helping tool in numerous domains. In this study, we present a systematic review of the utility of AI in DeFi in terms of impact, reliability, and security and conduct exhaustive analysis. The review was motivated by an in-depth investigation of recently published literature that prioritized AI and DeFi in their research. This research, like many prior studies, examined the articles in terms of impact, reliability, and security. In addition, a new relevance score is introduced to better comprehend the quality of the content. According to investigation, the combination of AI and DeFi is one of the trending research topics that lacks adequate interpretations of black-box methodologies. Furthermore, it was discovered that one of the primary issues in DeFi is security, and numerous technologies, including blockchain technology and machine learning approaches, have been used to minimize such challenges. We hope that the gap addressed throughout this review will give insights to future researchers and practitioners, ultimately leading to new research opportunities in AI to bridge the gap of trust between peers and make the integration of DeFi more agile in the near future.

Fate of AI for Smart City Services in India

With the rollout of the smart city initiative in India, this study explores potential risks and opportunities in adopting artificial intelligence (AI) for citizen services. The study deploys expert interview technique and the data collected from various sources are analyzed using qualitative analysis. It was found that AI implementation needs a critical examination of various socio-technological factors to avoid any undesirable impacts on citizens. Fairness, accountability, transparency, and ethics (FATE) play an important role during the design and execution of AI-based systems. This study provides vital insights into AI implications to smart city managers, citizen groups, and policymakers while delivering promised smart city experience. The study has social implications in terms of ensuring that proper guidelines are developed for using AI technology for citizen services, thereby bridging the ever-critical trust gap between citizens and city administration.

Barriers to patient enrolment in phase III cancer clinical trials: interviews with clinicians and pharmaceutical industry representatives

ObjectivesPhase III cancer clinical trials are expensive and time-consuming phases in drug development. Effective patient enrolment can reduce delays and save costs, offering patients an opportunity to benefit from innovative...

Trust in Science Now Deeply Polarized

No abstract available. Article truncated after 150 words. The Associated Press is reporting that Republicans' faith in science is falling as Democrats rely on it even more, with a trust gap in science and medicine widening substantially during the COVID-19 pandemic, new survey data shows (Figure 1) (1). Overall, 48% of Americans say they have "a great deal" of confidence in the scientific community, the 2021 General Social Survey data shows. Sixty-four percent of Democrats say that, compared with roughly half as many Republicans, 34%. The gap was much smaller in 2018, when 51% of Democrats and 42% of Republicans had high confidence. It's the largest gap in nearly five decades of polling by the General Social Survey, a widely respected trend survey conducted by NORC at the University of Chicago that has been measuring confidence in institutions since 1972. The most recent survey was conducted Dec. 1, 2020, through May 3, 2021, and includes interviews with 4,032 …

Trust in COVID-19 information sources and perceived risk among smokers: A nationally representative survey.

BackgroundPublic health officials have classified smoking as a risk factor for COVID-19 disease severity. Smokers generally have less trust in health experts than do nonsmokers, leading to reduced risk perceptions. This study addresses smokers’ trust in information sources about COVID-19 and how trust is associated with perceived COVID-19 susceptibility and severity among smokers.Methods and findingsA nationally representative sample of 1,223 current smokers were surveyed between October and November 2020, indicating their level of trust in COVID-19 information sources, and their perceptions of risk from COVID-19. Multiple differences in trustworthiness emerged; smokers trusted their personal doctor for information about COVID-19 more than other information sources, while news media were generally distrusted. In addition, the FDA was trusted less than the NIH and CDC. Several “trust gaps” were observed, indicating disparities in levels of trust associated with gender, ethnicity, education, and political orientation, which had the strongest association with trust of all factors. Political orientation was also a significant predictor of COVID-19 risk perceptions, but there was no independent effect of political orientation when accounting for trust, which was predictive of all risk perception outcomes.ConclusionsTrusted sources, such as personal doctors, may most effectively convey COVID-19 information across political orientations and sociodemographic groups. News media may be ineffective at informing smokers due to their low credibility. The results suggest that trust may explain the apparent effect of political orientation on COVID-19 risk perceptions. Implications for researchers, communication professionals, and policy makers are discussed.

Gender differences in preferences of adolescents: Evidence from a large-scale classroom experiment

In this study, we estimate unadjusted and adjusted gender gaps in time preference, risk attitudes, altruism, trust, trustworthiness, cooperation, and competitiveness using data on 1088 high school students from 53 classes. These data, collected by running incentivized experiments in Hungarian classrooms, are linked to an administrative data source on the students’ standardized test scores, grades and family background. After taking into account class fixed effects, we find that females are significantly more altruistic, but are less present biased, less risk tolerant, less trusting, less trustworthy, and less competitive than males. At the same time we do not observe significant gender differences in patience, time inconsistency and cooperation at the 5% significance level. We also show that most of these initial gender differences do not change even if we control for age, family background, cognitive skills and school grades in a regression framework. We risk over-control when we include the time spent on each task as well as the other preference domains in our regressions, but the gender gap remains significant in social preferences (altruism, trust and trustworthiness), present bias and competition.

Extending On-chain Trust to Off-chain -- Trustworthy Blockchain Data Collection using Trusted Execution Environment (TEE)

Blockchain creates a secure environment on top of strict cryptographic assumptions and rigorous security proofs. It permits on-chain interactions to achieve trustworthy properties such as traceability, transparency, and accountability. However, current blockchain trustworthiness is only confined to on-chain, creating a “trust gap” to the physical, off-chain environment. This is due to the lack of a scheme that can truthfully reflect the physical world in a real-time and consistent manner. Such an absence hinders further blockchain applications in the physical world, especially for the security-sensitive ones. In this paper, we propose a framework to extend blockchain trust from on-chain to off-chain, and take trustworthy vaccine tracing as an example scheme. Our scheme consists of 1) a Trusted Execution Environment (TEE)-enabled trusted environment monitoring system built with the Arm Cortex-M33 microcontroller that continuously senses the inside of a vaccine box through trusted sensors and generates anti-forgery data; and 2) a consistency protocol to upload the environment status data from the TEE system to blockchain in a truthful, real-time consistent, continuous and fault-tolerant fashion. Our security analysis indicates that no adversary can tamper with the vaccine in any way without being captured. We carry out an experiment to record the internal status of a vaccine shipping box during transportation, and the results indicate that the proposed system incurs an average latency of 84 ms in local sensing and processing followed by an average latency of 130 ms to have the sensed data transmitted to and been available in the blockchain.

Human Language Explanation for a Decision Making Agent via Automated Rationale Generation

Advances in reinforcement learning (RL) algorithms have made them become increasingly capable in many tasks in recent years.However, the vastmajority of RL algorithms are not readily interpretable off the shelf. Moreover, the task of generating explanations in the form of human language has not been sufficiently addressed for these RL algorithms in previous works. Human language explanations have the advantages of being easy to understand, and they can help increase the satisfaction experienced by the end user while using the product. In this paper, we propose a method for generating explanations in the form of free-text human language to help the end user better understand the behaviors of RL agents. Our work involves generating explanations for both single actions and sequences of actions. We also create an open dataset as a baseline for future research. Our proposed method is evaluated in two simulated environments: Pong and the Minimalistic Gridworld Environment (MiniGgrid). The results demonstrate that our models are able to consistently generate accurate rationales, which are highly correlated with the expert rationales. Hence, this work offers a solution for bridging the gap of trust encountered when employing RL agents in virtual world or real-world applications.

1212. COVID-19 and the Importance of Institutional Trust to Public Health

BackgroundInstitutional trust is a key component of the public health system’s effectiveness. However, the COVID-19 pandemic highlighted gaps in institutional trust and hesitation. Analysis was conducted to understand correlates of institutional trust to inform communication strategies for the ongoing pandemic and future public health crises.MethodsThe Roper Center for Public Opinion/ America’s Voice Project Coronavirus Index conducted a US online survey February 22-April 5, 2021 and included questions about COVID-19 experiences, attitudes and behaviors. Respondents also indicated trust in each of four institutions to provide accurate information about COVID-19: federal government, state government, CDC and national public health officials. Scores were summed to create an Institutional Trust (IT) index: the top third was classified as “High IT,” the middle third “Moderate IT” and the bottom third “Low IT.” Data were analyzed using χ2 tests, with z-tests for more granular between-group comparisons. ResultsThose with Low IT were significantly more likely than those with Moderate or High IT to be white, male, rural, politically conservative, married, and live with children under age 18. Low IT individuals were less likely to have been tested for COVID-19 themselves and less likely to know someone who had tested positive for COVID-19. However, Low IT respondents were more likely to have tested positive for COVID-19, even when controlling for their lower propensity to be tested. Low IT individuals were significantly more likely to have visited restaurants and stores in the past week and feel these activities posed no health risk; they also wore masks less often. Despite greater risk-taking, Low IT respondents were over five times more likely than High IT respondents to refuse the COVID-19 vaccine.ConclusionLow IT was associated with higher COVID-19 vaccine hesitancy as well as behavior that, at the time data was collected, put people at higher risk of contracting COVID-19. Public health officials should prioritize the development of more effective communications towards Low IT populations. Traditional methods of establishing message credibility may require modification in order to encourage Low IT individuals to participate in behaviors that enhance public health. Disclosures All Authors: No reported disclosures