On Rabbinic Authority

The digital era has transformed public health communication into a high-stakes balancing act. As misinformation and disinformation proliferate, health authorities face unprecedented challenges in maintaining public trust while adapting to the dynamic nature of scientific evidence. This plenary will explore how the public health community can uphold scientific integrity, communicate uncertainty with humility, and counter harmful narratives-without compromising the boldness required to drive meaningful action. The dual crisis erosion of trust and ideological polarization Public health institutions are navigating a landscape where facts increasingly compete with ideology. The COVID-19 pandemic laid bare the vulnerabilities and exposed the fragility of health communication systems: rapidly evolving guidelines, shifting recommendations, conflicting expert opinions, and politicized debates eroded public confidence and contributed to public skepticism. Today, misinformation thrives not only on social media but also through targeted disinformation campaigns that exploit emotional narratives and systemic inequities. Vulnerable populations-those with limited health literacy or preexisting distrust in institutions-are disproportionately affected, exacerbating disparities. Science, uncertainty, and controversy Scientific inquiry is inherently dynamic and iterative, yet this process often clashes with public expectations for definitive answers, meaning that its provisional nature can be at odds with public expectations for certainty. Two forms of controversy demand attention: 1. Divergent perspectives among researchers: Differences in methodologies, theoretical frameworks, or interpretations of data can lead to legitimate disagreements. For example, debates over screening guidelines or treatment protocols often reflect trade-offs between individual and population-level outcomes. 2. Evolving evidence: New discoveries can upend previous consensus, as seen with shifting understandings of COVID-19 transmission or vaccine efficacy. While this flexibility is a strength of science, it risks being mischaracterized as incompetence or dishonesty. These controversies underscore the need for transparency. Acknowledging uncertainty, while explaining how evidence is weighed, can mitigate perceptions of inconsistency. Openly discussing the process by which evidence is generated, evaluated, and sometimes revised can help demystify science and reduce perceptions of arbitrariness. Strategies for humble and bold communication Effective public health communication in this climate requires both humility and resolve: 1. Normalize uncertainty: Frame uncertainty as inherent to scientific progress. For instance, instead of dismissing past guidelines, clarify how new data refined recommendations. 2. Anticipate, address, and prebunk misinformation: Proactively address common myths by equipping audiences with critical thinking tools, improving systems with an emphasis on preemptive messaging to reduce susceptibility to false claims. 3. Leverage trusted messengers: Collaborate with community leaders, healthcare providers, and influencers who resonate with specific demographics. Trust is built through cultural relevance and consistency. 4. Prioritize clarity, accessibility, and nuance: Use straightforward, plain language and clear explanations to convey complex information without oversimplifying and to explain risk-benefit analyses and evidence hierarchies. Avoid absolutist language that may later require retraction. A call to action for EUPHA and beyond This plenary marks the launch of EUPHA’s long-term initiative and sustained commitment to strengthen public health communication ecosystems. Panelists will be invited to: 1. Share practical experiences in managing scientific controversies and public scepticism. 2. Share case studies on navigating controversies (e.g., vaccine hesitancy, climate health policies). 3. Explore innovative tools for monitoring and countering misinformation, such as AI-driven misinformation monitoring or participatory community dialogues. 4. Reflect on personal experiences where humility in communication preserved trust during crises, particularly in moments of uncertainty or evolving evidence. The session will prioritize interactive dialogue, recognizing that solutions must be co-created with diverse stakeholders. Post-conference, EUPHA will establish working groups to develop practical, actionable frameworks for addressing misinformation, training health professionals in risk communication, and advocating for evidence-informed policy. Moderators Charlotte Marchandise Executive Director EUPHA Orkan Okan President EUPHA Health literacy section Speakers/Panellists Götz Gottschalk Head of Health, YouTube (Google), Germany Seema Yasmin Emmy Award-winning journalist, Clinical Assistant Professor, Medicine, Stanford University, USA Sanna Vesikansa Director, Crisis Services MIELI Mental Health, Finland

The COVID-19 pandemic was not just a public health crisis but a crisis of confidence—one that revealed the fragility of public trust in scientific expertise, democratic institutional governance, and the very mechanisms by which societies establish trust and determine truth. The pandemic brought many existing geopolitical tensions to the fore, with several state-aligned actors from various countries—including both authoritarian regimes and liberal democracies—taking to social media to engage in what could be seen as aggressive vaccine diplomacy as well as covert operations targeting rivals. Russian state-aligned social media actors seized on this moment of uncertainty—not to promote any coherent counter-narrative, but to make coherence itself elusive. Their strategy was not to persuade but to confuse, amplifying deep-seated anxieties and contradictions, fostering ideological polarization, and transforming pandemic discourse into yet another site of contestation. By strategically amplifying both vaccine skepticism and criticism of government failure and overreach, this operation did not merely seek to instill doubt in a particular version of reality. Rather, it aimed to erode faith in the very possibility of a shared reality altogether. This deliberate amplification of uncertainties carries consequences that extend far beyond COVID-19.

The integration of generative artificial intelligence (AI) into journalism marks a transformative era in content creation, newsroom automation, and audience engagement. Tools such as large language models and generative image and video systems now enable rapid production of headlines, articles, infographics, and multimedia packages, dramatically improving editorial efficiency. However, this innovation introduces profound ethical questions that challenge long-standing journalistic values of truth, transparency, accountability, and public trust. This paper explores the ethical implications of generative AI in journalism from both philosophical and operational perspectives, aiming to define a responsible framework for its adoption. At a broad level, the study examines how AI-generated content risks eroding trust when its authorship is ambiguous or when factual inconsistencies go unrecognized. It also addresses the societal impact of automating editorial decisions, which may reflect algorithmic bias, reduce diversity of perspectives, or marginalize human editorial judgment. Narrowing its focus, the paper reviews emerging industry practices in disclosure, AI audit trails, and newsroom policy formation to govern AI usage responsibly. Drawing from real-world case studies involving synthetic anchors, AI-generated news summaries, and deepfake reports, the paper highlights both the potential and perils of AI augmentation in journalism. It also evaluates regulatory proposals and ethical guidelines issued by professional journalism bodies and media councils. Ultimately, the paper advocates for a hybrid model of human-AI collaboration where journalistic oversight, algorithmic transparency, and ethical foresight coalesce to maintain credibility. This model ensures that while innovation is embraced, truth and public communication trust remain the cornerstone of responsible journalism in the digital age.

An experimental study of public trust in AI chatbots in the public sector

The integration of Artificial Intelligence (AI) in smart cities has transformed urban governance, enhancing efficiency in public services, infrastructure management, and decision-making. However, the widespread use of AI for data collection and analysis raises significant challenges related to privacy, algorithmic bias, transparency, and public trust. Without proper governance, AI systems risk exacerbating inequalities, infringing on citizen rights, and reducing accountability in automated decision-making. This paper explores how AI-driven frameworks can enhance data governance while ensuring privacy protection, algorithmic fairness, and citizen empowerment. Key strategies include federated learning to enable decentralized data processing, differential privacy to protect individual identities, and explainable AI to increase transparency in automated decisions. Additionally, bias detection mechanisms and algorithmic audits are essential to prevent discrimination in AI-driven urban systems. Public trust is crucial in smart city initiatives, requiring citizen engagement models, participatory AI councils, and transparent data-sharing policies. Case studies illustrate how open data initiatives, AI-driven services, and digital innovation can enhance public service delivery and civic engagement when guided by strong governance and ethical data management. The paper proposes a comprehensive governance framework integrating privacy-centric AI, fairness-aware algorithms, and public engagement strategies to ensure sustainable, transparent, and accountable AI-driven urban ecosystems. The findings suggest that aligning technological innovation with inclusive policies and capacity-building not only improves urban efficiency and resilience but also builds public trust and empowerment. By aligning technological advancements with ethical and legal safeguards, smart cities can optimize AI’s potential while maintaining public trust and regulatory compliance.

The rapid advancement of artificial intelligence (AI) technologies has highlighted the critical importance of data privacy within the framework of international trade law. This study aims to explore the dynamic interplay between AI innovation and data privacy regulations in the context of global trade. The primary objective is to understand how international data privacy laws influence AI development within trade activities and how AI advancements, in turn, affect data privacy compliance and public trust in international markets. This study employs system dynamics modeling to analyze the complex interplay between artificial intelligence (AI) innovation and data privacy regulations within the context of international trade law. These variables include AI innovation level (AIL), investment in AI R&D (I AI R&D), data privacy compliance level (DPC), public trust in AI (PTAI), regulatory framework strength (RFS), compliance costs (CCs), innovation incentives (II), and cross-border data flow regulations (CBDFRs). By modeling these interactions, the study seeks to provide insights into balancing technological innovation with robust privacy protections specific to international trade. The focus is on the interdependencies between AI research and development (R&D) investments, regulatory frameworks tailored to trade, data privacy compliance levels, public trust, and associated compliance costs within the context of international trade law. The model also considers the impact of cross-border data flow regulations and innovation incentives on these dynamics. A system dynamics approach was employed to create a numerical model that simulates the relationships and feedback loops among the identified variables. Initial values were set on the basis of plausible estimates, and the model was run over five years to observe the trajectories of each variable under various regulatory and innovation scenarios.

As part of its modernisation and economic diversification policies, Saudi Arabia is building smart, sustainable cities intended to improve quality of life and meet environmental goals. However, involving the public in urban planning remains complex, with traditional methods often proving expensive, time-consuming, and inaccessible to many groups. Integrating artificial intelligence (AI) into public participation may help to address these limitations. This study explores whether Saudi residents are ready to engage with AI-driven tools in urban planning, how they prefer to interact with them, and what ethical concerns may arise. Using a quantitative, survey-based approach, the study collected data from 232 Saudi residents using non-probability stratified sampling. The survey assessed demographic influences on AI readiness, preferred engagement methods, and perceptions of ethical risks. The results showed a strong willingness among participants (200 respondents, 86%)—especially younger and university-educated respondents—to engage through AI platforms. Visual tools such as image and video analysis were the most preferred (96 respondents, 41%), while chatbots were less favoured (16 respondents, 17%). However, concerns were raised about privacy (76 respondents, 33%), bias (52 respondents, 22%), and over-reliance on technology (84 respondents, 36%). By exploring the intersection of generative AI and participatory urban governance, this study contributes directly to the discourse on inclusive smart city development. The research also offers insights into how AI-driven public engagement tools can be integrated into urban planning workflows to enhance the design, governance, and performance of the built environment. The findings suggest that AI has the potential to improve inclusivity and responsiveness in urban planning, but that its success depends on public trust, ethical safeguards, and the thoughtful design of accessible, user-friendly engagement platforms.

This systematic review, based on 37 articles, explores the role of artificial intelligence (AI) in criminal justice, focusing on its applications in predictive policing, judicial risk assessments, and surveillance, as well as the associated ethical and regulatory challenges. AI has demonstrated substantial potential for improving efficiency and accuracy in criminal justice systems, from optimizing law enforcement resource allocation to providing data-driven risk assessments that support judicial decisions. However, the review identifies significant ethical issues, especially related to algorithmic bias, which can perpetuate existing societal inequalities and disproportionately affect marginalized communities. Concerns around transparency and accountability are prevalent, as the "black-box" nature of many AI algorithms complicates public understanding and trust in AI-driven outcomes. Surveillance tools, including facial recognition and behavioral analysis, enhance real-time threat detection but raise privacy and civil rights concerns, highlighting the need for regulatory oversight. Gaps in legal frameworks suggest the urgency for standardized policies that address data privacy, algorithmic fairness, and accountability in AI applications. The findings underscore that interdisciplinary collaboration, transparent practices, and comprehensive regulatory measures are essential to responsibly integrate AI into criminal justice, balancing technological advancements with justice, equity, and public trust.

Aim/Purpose: This study aims to investigate the influence of e-government public services on public trust at the local government level, addressing the pressing need to understand the factors shaping citizen perceptions and trust in government institutions. Background: With the proliferation of e-government initiatives worldwide, governments are increasingly turning to digital solutions to enhance public service delivery and promote transparency. However, despite the potential benefits, there remains a gap in understanding how these initiatives impact public trust in government institutions, particularly at the local level. This study seeks to address this gap by examining the relationship between e-government service quality, individual perceptions, and public trust, providing valuable insights into the complexities of citizen-government interactions in the digital age. Methodology: Employing a quantitative approach, this study utilises surveys distributed to users of e-government services in one of the regencies in Indonesia. The sample consists of 278 individuals. Data analysis is conducted using Partial Least Squares Structural Equation Modelling, allowing for the exploration of relationships among variables and their influence on public trust. Contribution: This study provides insights into the factors influencing public trust in e-government services at the local government level, offering a nuanced understanding of the relationship between service quality, individual perceptions, and public trust. Findings: This study emphasises information quality and service quality in e-government-based public services as crucial determinants of individual perception in rural areas. Interestingly, system quality in e-government services has no influence on individual perception. In the individual perception, perceived security and privacy emerge as the strongest antecedent of public trust, highlighting the need to guarantee secure and private services for citizens in rural areas. These findings emphasise the importance of prioritising high-quality information, excellent service delivery, and robust security measures to foster and sustain public trust in e-government services. Recommendations for Practitioners: Practitioners must prioritise enhancing the quality of e-government services due to their significant impact on individual perception, leading to higher public trust. Government agencies must ensure reliability, responsiveness, and the effective fulfilment of user needs. Additionally, upholding high standards of information quality in e-government services by delivering accurate, relevant, and timely information remains crucial. Strengthening security measures through robust protocols such as data encryption and secure authentication becomes essential for protecting user data. With that in mind, the authors believe that public trust in government would escalate. Recommendation for Researchers: Researchers could investigate the relation between system quality in e-government services and individual perception in different rural settings. Longitudinal studies could also elucidate how evolving service quality, information quality, and security measures impact user satisfaction and trust over time. Comparative studies across regions or countries can reveal cultural and contextual differences in individual perceptions, identifying both universal principles and region-specific strategies for e-government platforms. Analysing user behaviour and preferences across various demographic groups can inform targeted interventions. Furthermore, examining the potential of emerging technologies such as blockchain or artificial intelligence in enhancing e-government service delivery, security, and user engagement remains an interesting topic. Impact on Society: This study’s findings have significant implications for fostering public trust in government institutions, ultimately strengthening democracy and citizen-government relations. By understanding how e-government initiatives influence public trust, policymakers can make informed decisions to improve service delivery, enhance citizen engagement, and promote transparency, thus contributing to more resilient and accountable governance structures. Future Research: Future research could opt for longitudinal studies to evaluate the long-term effects of enhancements in service quality, information quality, and security. Cross-cultural investigations can uncover universal principles and contextual differences in user experiences, supporting global e-government strategies in rural areas. Future research could also improve the research model by adding more variables, such as risk aversion or fear of job loss, to gauge individual perceptions.

The importance of the assurance that “humans are still in the decision loop” for public trust in artificial intelligence: Evidence from an online experiment

Artificial intelligence (AI) is rapidly expanding in myriad industries and systems. This study sought to investigate public trust in using AI in the criminal court process. While previous research has identified factors that influence trust in AI, such as perceived accuracy and transparency of algorithms, less is known about the role of influential leaders—such as judges—in shaping public trust in new technology. This study examined the relationship between locus of control, anthropomorphism, cultural values, and perceived trust in AI. Participants completed a survey assessing their perceptions of trust in AI in determining bail eligibility, bail fines and fees, sentencing length, sentencing fines and fees, and writing legal documents (e.g., findings and disposition). Participants were more likely to trust AI performing financial calculations rather than determining bail eligibility, sentence length, or drafting legal documents. Participants’ comfort with AI in decision-making also depended on their perceptions of judges’ trust in AI, and they expressed concerns about AI perpetuating bias and the need for extensive testing to ensure accuracy. Interestingly, no significant association was found with other participant characteristics (e.g., locus of control, anthropomorphism, or cultural values). This study contributes to the literature by highlighting the role of judges as influential leaders in shaping public trust in AI and examining the influence of individual differences on trust in AI. The findings also help inform the development of recommended practices and ethical guidelines for the responsible use of AI in the courts.

Nonprofit organizations occupy a pivotal position in national disaster communication, often serving as the most accessible and trusted intermediaries for at-risk populations. Yet their ability to deliver timely, actionable, locally relevant, and credible information is constrained by limited staffing, multilingual audiences, uneven digital capacity, and rapidly evolving situational demands. Recent advances in artificial intelligence (AI)—including automated triage and routing, conversational agents, machine translation, summarization, and rumor-detection pipelines—offer new affordances for reducing message latency, increasing message personalization, and strengthening credibility cues during high-uncertainty events. This study systematically evaluates whether, and through what mechanisms, AI-enabled communication tools enhance nonprofit digital outreach effectiveness during national disasters. Using a quantitative, cross-sectional, multiple–case design (N = 236 staff respondents across five nonprofit organizations), the study operationalizes AI Adoption Intensity, Message Relevance, Public Trust, Digital Outreach Effectiveness, Digital Readiness, and Disaster Severity with validated five-point Likert scales demonstrating strong reliability (α = .84–.92), composite reliability (CR ≥ .88), and convergent validity (AVE ≥ .59). Descriptive analyses indicate substantial variance in AI adoption (M = 3.22, SD = 0.86) and readiness (M = 3.35, SD = 0.83), providing analytic leverage to examine their relationships with outreach outcomes (M = 3.81, SD = 0.67). Hierarchical regression results show that AI Adoption Intensity is positively associated with Digital Outreach Effectiveness (β = .23, SE = .05, p < .001), improving model fit by ΔR² = .11 after accounting for organizational controls. When Message Relevance and Public Trust are included as theoretically proximal predictors, both emerge as strong determinants of effectiveness (MR: β = .36, p < .001; PT: β = .22, p < .001). Bootstrapped mediation analyses (5,000 resamples) confirm that AI’s association with outreach effectiveness is partially transmitted through Message Relevance (β_indirect = .14; 95% CI [.09, .21]) and Public Trust (β_indirect = .08; 95% CI [.04, .14]), yielding a total indirect effect of .22. The residual direct effect of AI remains significant (β = .09; 95% CI [.01, .17]), indicating partial—but not full—mediation. Moderation models further reveal that Digital Readiness amplifies AI’s marginal benefits (interaction β = .12, p < .01), with the AI → effectiveness slope increasing from non-significant at low readiness to β = .31 (p < .001) at high readiness. Conversely, Disaster Severity attenuates AI’s returns (interaction β = −.10, p < .05), as extreme operational strain and verification bottlenecks reduce the translation of algorithmic speed into perceived clarity and trust.

- Artificial intelligence (AI) is rapidly transforming government operations and influencing social and economic systems across the globe. While AI delivers substantial benefits such as enhanced financial services, improved medical diagnostics, and accelerated decision-making, it also presents critical ethical, legal, and societal challenges. These challenges include algorithmic bias, limited transparency, privacy risks, and unclear accountability for AI-driven outcomes. The long-term success and widespread adoption of AI technologies depend heavily on public trust, which may be undermined by ineffective or ambiguous regulatory frameworks. This study conducts a comparative analysis of AI governance approaches adopted by leading AI-driven regions, including the United States, the European Union, China, and emerging AI hubs. It evaluates regulatory frameworks, ethical standards, and institutional practices using benchmarks such as transparency, fairness, accountability, and stakeholder participation. The analysis is based on legislation, government initiatives, international guidelines, and scholarly literature. The findings indicate that public trust in AI is closely linked to policies that promote openness, enforce accountability, and encourage multi-stakeholder involvement, whereas inconsistent or vague regulations create uncertainty and impede AI diffusion. Keywords— Artificial Intelligence Governance, Public Trust, AI Regulation, Ethics, Policy Analysis

Industrial news

Advancement in information technology continues to evolve especially in the field of artificial intelligence (AI). Research studies have been conducted to evaluate the perceptions of Americans on the development and utilization of AI technology and if it is appropriate to use AI in public administrative duties. The research revealed that society is fragmented regarding the acceptance of AI, and whether AI decisions could have long‐term effects on the labor industry, legal system, and national security. The 2018 AI Public Opinion Survey revealed significant concerns among the American public regarding AI, yet also a recognition of its promise. The goal of this article is to further develop a governance framework for AI that considers the importance of public trust in AI policy. First, it discusses the necessity of public trust for the effective governance of emergent technology. Then, it evaluates public opinion on AI technology that specifically pertains to governance. The article concludes with a discussion of why public trust is central to good AI governance.