Empowering Social Media Users With Ethical AI

The Readiness assessment methodology (RAM) is a macro level instrument that will help countries understand where they stand on the scale of preparedness to implement AI ethically and responsibly for all their citizens, in so doing highlighting what institutional and regulatory changes are needed. The outputs of the RAM will help UNESCO tailor the capacity building efforts to the needs of specific countries. Capacity here refers to the ability to assess AI systems in line with the Recommendation, the presence of requisite and appropriate human capital, and infrastructure, policies, and regulations to address the challenges brought about by AI technologies and ensure that people and their interests are always at the center of AI development. In November 2021, the 193 Member States of UNESCO signed the Recommendation on the Ethics of Artificial Intelligence, the first global normative instrument in its domain. The Recommendation serves as a comprehensive and actionable framework for the ethical development and use of AI, encompassing the full spectrum of human rights. It does so by maintaining focus on all stages of the AI system lifecycle. Beyond elaborating the values and principles that should guide the ethical design, development and use of AI, the Recommendation lays out the actions required from Member States to ensure the upholding of such values and principles, through advocating for effective regulation and providing recommendations in various essential policy areas, such as gender, the environment, and communication and information. The Recommendation mandated the development of two key tools, the Readiness Assessment Methodology (RAM) and the Ethical Impact Assessment (EIA), which form the core pillars of the implementation. These tools both aim to assess and promote the resilience of existing laws, policies and institutions to AI implementation in the country, as well as the alignment of AI systems with the values and principles set out in the Recommendation. The goal of this document is to provide more information on the Readiness Assessment Methodology, lay out its various dimensions, and detail the work plan for the implementing countries, including the type of entities that need to be involved, responsibilities of each entity, and the split of work between UNESCO and the implementing country. UNESCO Catno: 0000385198 Doc code: SHS/REI/BIO/REC-AIETHICS-TOOL/AR https://unesdoc.unesco.org/ark:/48223/pf0000385198_ara

Artificial Intelligence (AI) systems are embedded in institutions that are not diverse, that are inequitable, unjust, and exclusionary. How do we address the interface between AI systems and an unjust world, in service to human flourishing? One mechanism for addressing AI Ethics is AI Ethics Education: training those who will build, use, and/or be subject to AI systems to have clear moral reasoning, make responsible decisions, and take right actions. This paper presents, as part of a larger project, work on what AI Ethics instructors currently do and how they describe their motivating concerns. I find that although AI Ethics content and pedagogy is varied, there are some common motivating concerns particular to this kind of teaching, which may be useful in structuring future guidance for new AI Ethics teachers, evaluating existing pedagogy, guiding research on new pedagogies, and promoting discussion with the AI Ethics community.

This book offers a unique interdisciplinary perspective on the ethics of 'artificial intelligence' – autonomous, intelligent, (and connected) systems, or AISs, applying principles of social cognition to understand the social and ethical issues associated with the creation, adoption, and implementation of AISs. As humans become entangled in sociotechnical systems defined by human and artificial agents, there is a pressing need to understand how trust is created, used, and abused. Compounding the difficulty in answering these questions, stakeholders directly or indirectly affected by these systems differ in their motivations, understanding, and values. This volume provides a comprehensive resource to help stakeholders understand ethical issues of designing and implementing AISs using an ethical sensemaking approach. Starting with the general technical affordances of AIS, Dr. Jordan Richard Schoenherr considers the features of system design relating data integrity, selection and interpretation of algorithms, and the evolution processes that drive AISs innovation as a sociotechnological system. The poles of technophobia (algorithmic aversion) and technophilia (algorithmic preference) in the public perception of AISs are then described and considered against existing evidence, including issues ranging from the displacement and re-education needs of the human workforce, the impact of use of technology on interpersonal accord, and surveillance and cybersecurity. Ethical frameworks that provide tools for evaluating the values and outcomes of AISs are then reviewed, and how they can be aligned with ethical sensemaking processes identified by psychological science is explored. Finally, these disparate threads are brought together in a design framework. Also including sections on policies and guideline, gaming and social media, and Eastern philosophical frameworks, this is fascinating reading for students and academics in psychology, computer science, philosophy, and related areas, as well as professionals such as policy makers and those working with AI systems.

This study concerns the sociotechnical bases of human autonomy. Drawing on recent literature on AI ethics, philosophical literature on dimensions of autonomy, and on independent philosophical scrutiny, we first propose a multi-dimensional model of human autonomy and then discuss how AI systems can support or hinder human autonomy. What emerges is a philosophically motivated picture of autonomy and of the normative requirements personal autonomy poses in the context of algorithmic systems. Ranging from consent to data collection and processing, to computational tasks and interface design, to institutional and societal considerations, various aspects related to sociotechnical systems must be accounted for in order to get the full picture of potential effects of AI systems on human autonomy. It is clear how human agents can, for example, via coercion or manipulation, hinder each other’s autonomy, or how they can respect each other’s autonomy. AI systems can promote or hinder human autonomy, but can they literally respect or disrespect a person’s autonomy? We argue for a philosophical view according to which AI systems—while not moral agents or bearers of duties, and unable to literally respect or disrespect—are governed by so-called “ought-to-be norms.” This explains the normativity at stake with AI systems. The responsible people (designers, users, etc.) have duties and ought-to-do norms, which correspond to these ought-to-be norms.

Background: In an era where digital connectivity dominates daily life, social media has become an integral component of communication and identity formation, particularly among young adults. However, excessive engagement is increasingly associated with adverse psychological outcomes, including emotional instability. Fear of Missing Out (FOMO) has emerged as a central mechanism linking social media involvement with emotional disturbances, highlighting the need to explore its mediating role in this relationship. Objective: This study aimed to investigate the relationships among social media use, FOMO, and emotional dysregulation in young adults, and to examine whether FOMO mediates the link between social media use and emotional dysregulation while accounting for demographic factors. Methods: A quantitative, cross-sectional design was applied among 322 young adults aged 18–25 years (44.7% males, 55.3% females). Participants completed standardized instruments: the Social Media Use Scale (SMUS; α = .922), the Fear of Missing Out Scale (FOMOS; α = .826), and the Difficulties in Emotion Regulation Scale—Short Form (DERS-16; α = .920). Data were analyzed using Pearson correlation, simple linear regression, hierarchical multiple regression, and mediation analysis through Hayes’ PROCESS macro (Model 4) with 5,000 bootstrapped samples. Results: Social media usage significantly predicted emotional dysregulation (B = 0.29, SE = 0.03, β = .51, p < .001), explaining 26% of its variance. FOMO was positively correlated with both social media use (r = .52, p < .001) and emotional dysregulation (r = .58, p < .001) and partially mediated their relationship (indirect effect B = 0.13, 95% CI [0.09, 0.18]). Age showed a weak negative effect (B = −0.043, p = .040), whereas gender, education, and socioeconomic status were non-significant predictors. Conclusion: The findings demonstrate that FOMO significantly mediates the association between social media use and emotional dysregulation among young adults, emphasizing that psychological mechanisms outweigh demographic predictors. These results suggest that interventions promoting emotional regulation and mindful social media use could mitigate digital-era emotional vulnerabilities.

AI and robot technology of the new era will have a major impact on human development in future. Such technologies are beginning to raise some basic issues about how to use these systems and what are potential risks and how to control them. This chapter explores one of AI and robotics popular and controversial topic—AI ethics. First, we introduce AI ethics with Asimov’s Three Laws of Robotics. Next, we study major aspects and concerns related to AI ethics include: robots ethics, robot rights, moral agents, opaqueness of AI systems, privacy & AI monitoring, automation and employment, prejudices in AI systems, responsibility for autonomous machines, and international AI ethic policy.

Solace in Social Media: Women Unite Under COVID-19

This study is motivated by the growing discourse on artificial intelligence (AI) ethics that has not yet been fully operationalized in practice, particularly in the health and education sectors in the era of generative AI. It aims to synthesize the literature on the operationalization of AI ethics in order to propose a new governance model that is more adaptive and context-sensitive. A structured thematic synthesis was conducted on peer-reviewed studies published between 2020 and 2025. The analysis reveals a central paradox: a strong global consensus on the principles of justice, transparency, accountability, and privacy stands in stark contrast to significant gaps in their implementation. The emerging challenges are highly context-dependent: in the health sector, they center on technical safety and relational accountability, whereas in the education sector they are more closely related to epistemic integrity and equitable access. The advent of generative AI exacerbates these issues by introducing new systemic risks, such as large-scale hallucinations and an increasingly profound crisis of clarity. In response to the limitations of static compliance-based approaches, this study proposes the Adaptive and Layered AI Governance Model (ALAIGoM), a model of adaptive and layered AI governance that integrates operationalized ethical principles, multi-level governance structures, and a continuous ethics life cycle to enable context-sensitive, adaptive oversight. The model is grounded in proactive trust-building, human-centered design, and mechanisms that ensure acceptability. Overall, this study offers a coherent roadmap for reconfiguring AI ethics from a mere normative checklist into an embedded, adaptive, and responsive governance process.

Navigating Social Media in #Ophthalmology

Scientific and public debates on the ethical aspects of AI development and deployment often end up focusing on trust in AI systems, rather than on their trustworthiness. This paper argues that actual trust should not be the focus of the debate in AI ethics or the goal of the responsible design, deployment, and assessment of AI systems. The argument will insist on three distinct—although interrelated—points. First, I will argue that trust is a complex psychological phenomenon that is influenced by many contextual and non-rational factors that may have little to do with AI systems’ actual trustworthiness. Then, I will show that some widely employed strategies to foster trust in AI are ethically questionable and hardly compatible with the trustworthy AI paradigm. Finally, I will focus on the fact that trust might lead to unmonitored reliance on systems whose risks are not negligible and, in many cases, largely unknown.

Background There are growing concerns that social media may negatively impact adolescents’ mental health. However, most studies are cross-sectional, or short-term longitudinal studies, limiting conclusions about long-term effects. This study investigates whether social media use at age 15/16 is associated with symptoms of depression and anxiety five years later. It also explores differences by gender, socioeconomic status (SES), and physical activity. Methods Data come from a nationally representative cohort of Swedish adolescents. In 2017, 5535 students in grade 9 (age 15/16) completed a self-report survey. In 2022, 3193 participated in follow-up. Social media use (hours/day) was self-reported; depression and anxiety were measured using PHQ-4. Moderation by gender, SES, and physical activity were tested. Linear regression models were estimated. Results In unadjusted models, social media use at age 15/16 was positively associated with anxiety and depression five years later, but these associations were no longer significant after adjusting for confounders. No significant moderation by gender, SES or physical activity was observed. Conclusions Among Swedish adolescents, time spent on social media is not associated with more symptoms of anxiety or depression five years later. If the literature reaches consensus that social media use does not have long-term effects on mental health, an important implication is that efforts to reduce social media use by adolescents are unlikely to prevent mental health problems. Key messages • Frequent social media use in adolescence is not linked to symptoms of depression and anxiety in young adulthood. • Future longitudinal studies should examine which types of social media activities or experiences may be associated with negative outcomes.

Spatial Patterns of Purposeful Social Networking and Socioeconomic Influences: Towards a Mature, Inclusive Networked Society in the U.S.

This paper examines the ethical solutions raised in response to OpenAI’s language model Generative Pre-trained Transformer-3 (GPT-3) a year and a half from its release. I argue that hype and fear about GPT-3, even within the Natural Language Processing (NLP) industry and AI ethics, have often been underpinned by technologically deterministic perspectives. These perspectives emphasise the autonomy of the language model rather than the autonomy of human actors in AI systems. I highlight the existence of deterministic perspectives in the current AI discourse (which range from technological utopianism to dystopianism), with a specific focus on the two issues of: (1) GPT-3’s potential intentional misuse for manipulation and (2) unintentional harm caused by bias. In response, I find that a contextual approach to GPT-3, which is centred upon wider ecologies of societal harm and benefit, human autonomy, and human values, illuminates practical solutions to concerns about manipulation and bias. Additionally, although OpenAI’s newest 2022 language model InstructGPT represents a small step in reducing toxic language and aligning GPT-3 with user intent, it does not provide any compelling solutions to manipulation or bias. Therefore, I argue that solutions to address these issues must focus on organisational settings as a precondition for ethical decision-making in AI, and high-quality curated datasets as a precondition for less harmful language model outputs.

As China rapidly advances in AI innovation and development, especially in frontier AI, its regulatory and ethical frameworks are under increasing pressure to ensure that technological progress aligns with human interests and societal values. This Article argues that AI value alignment—the process of ensuring AI systems act in accordance with human values, norms, and ethical principles—should be adopted as a strategic pillar in China’s evolving AI governance architecture. While China has already established a comprehensive legal, ethical, and self-regulatory landscape to address AI risks, these mechanisms often rely on reactive enforcement and external compliance. In contrast, AI value alignment offers a proactive, intrinsic approach that embeds safety and ethical constraints directly into AI systems, making them safer, more trustworthy, and responsive to human needs. This study begins by mapping China’s current AI governance landscape, including national legislation such as the Cybersecurity Law, Personal Information Protection Law, and a growing set of regulations targeted at algorithms and generative AI. It also evaluates China’s normative commitments, such as the “human-centric” and “tech for good” principles articulated in national policy documents, and the increasing role of corporate self-regulation among major technology firms. While commendable in scope and ambition, these governance mechanisms often fall short in ensuring that AI behavior aligns with safety constraints and ethical intent—particularly when AI systems (such as agentic AI) become more autonomous and capable. This gap highlights the urgent need for a systematic value alignment strategy. The Article then delves into the conceptual and technical foundations of AI value alignment, identifying both engineering challenges—such as reward misspecification, data bias, and model deception—and normative dilemmas, including moral pluralism, value aggregation, and dynamic ethics. Special attention is paid to frontier models like large language models and artificial general intelligence (AGI), which pose alignment challenges at a scale previously unseen. Drawing on contemporary alignment techniques such as RLHF (Reinforcement Learning from Human Feedback) and principle-based alignment, such as Anthropic’s Constitutional AI, the Article explores their limitations and calls for a more diversified, interdisciplinary, and forward-looking alignment research agenda. Finally, the Article offers a roadmap for operationalizing AI value alignment across three key governance domains: Law and regulation, ethical norms, and industry self-regulation. Recommendations include the incorporation of alignment assessments into regulatory filings, the development of technical standards for value alignment and ethics-by-design guidelines, and institutional investments in safety and alignment research. The Article concludes by asserting that value alignment is not merely a technical safeguard but a governance imperative for the age of autonomous AI and agentic AI. By integrating alignment into its AI governance strategy, China can not only enhance domestic safety and public trust but also better coordinate with global AI ethics and safety initiatives—ultimately contributing to the shared goal of human-aligned and beneficial artificial intelligence.

Background: Trustworthy AI serves as a foundational pillar for two major AI ethics conferences: AIES and FAccT. Current research often adopts techno-centric approaches, focusing primarily on technical attributes such as accuracy, reliability, robustness, and fairness, while overlooking the sociotechnical dimensions critical to understanding AI trustworthiness in real-world contexts. Objectives: This scoping review aims to examine how the AIES and FAccT communities conceptualize, measure, and validate AI trustworthiness, identifying major gaps and opportunities for advancing a holistic understanding of trustworthy AI systems. Methods: We conduct a scoping review of the AIES and FAccT conference proceedings to date, systematically analyzing how trustworthiness is defined, operationalized, and applied across different research domains. Our analysis focuses on conceptualization approaches, measurement methods, verification and validation techniques, application areas, and underlying values. Results: While significant progress has been made in defining technical attributes such as transparency, accountability, and robustness, our findings reveal critical gaps. Current research often predominantly emphasizes technical precision at the expense of social and ethical considerations. The sociotechnical nature of AI systems remains less explored and trustworthiness emerges as a contested concept shaped by those with the power to define it. Conclusions: An interdisciplinary approach combining technical rigor with social, cultural, and institutional considerations is essential for advancing trustworthy AI. We propose actionable measures for the AI ethics community to adopt holistic frameworks that genuinely address the complex interplay between AI systems and society, ultimately promoting responsible technological development that benefits all stakeholders.