An Artificial Intelligence Security Framework

Artificial intelligence (AI) for health is a rapidly evolving field, with many potential benefits and risks, but governing bodies are struggling to keep pace. This lag is further fuelled by a lack of comprehensive international guidance on how to ensure AI for health complies with ethical norms and human rights standards. To address this gap, WHO has published guidance on the ethics and governance of AI for health. The guidance presents six consensus principles and a series of recommendations. Three themes stand out: increase patient and public involvement; eliminate gaps in digital literacy, as well as biases that infringe on human autonomy and exacerbate health inequities; and establish frameworks for appropriate governance and accountability. These themes are not new, overlapping with the OECD's AI principles, but it is the first comprehensive guidance from a global body to establish such principles for health. The guidance encourages AI developers and other relevant stakeholders to engage with end users—patients, communities, and the public—at all stages of the AI pathway (from design to deployment) and over proposed changes to how health data will be collected or used. There are examples of where this is already taking place. The UK Health Data Research Alliance, convened by Health Data Research UK (HDRUK), is an independent alliance of large-scale health data custodians with the goal of developing and disseminating best practice for the ethical and responsible use of health datasets. Key to this is interaction with patients and disease advocacy communities to understand their interests and values regarding health data use, and to share the work of the Alliance. HDRUK have also set up a patient and public involvement group to help to inform their COVID-19 strategy. While the WHO guidance encourages post-deployment feedback on health AI, this requires structured processes to ensure feedback is provided in a transparent way. End-user engagement at inception and feedback from real-world use are pivotal to ensure that users' needs are at the heart of AI design, and that safety, efficacy, and functionality are monitored and improved. Open discussions can also help to take the public's temperature on controversial topics in the field such as the trade-off between individual privacy protection and data sharing to benefit science—a particularly pertinent discussion during COVID-19. But such discussions can be hindered by gaps in digital literacy among the public. Furthermore, concerns that biases in data and algorithms will entrench existing health inequities, and that any advancements made will not be equal among those contributing their data—especially those in low-income and middle-income countries (LMICs)—engender mistrust. While the WHO guidance delves into these issues, it largely focuses on addressing digital literacy among the health-care workforce, leaving identification of ways to address biases as a suggested research topic. Improving digital literacy through education and resources is key so patients, communities and the public can make informed, autonomous decisions about their health. Likewise, robust research at the intersection of AI, ethics, and health in LMICs is imperative to drive equitable access to health AI. All of these steps require reforms, not only to support universal health coverage but also to ensure AI tools are adequately assessed and validated for fairness, and developers and technology companies are held to account for errors and biased outputs. But the guidance points out that technology companies setting out (and holding themselves accountable to) their own ethical norms and standards is not the way. Rather, AI should be audited and quality controlled through independent external oversight. A navigation aid developed by Urs Gasser and colleagues for digital tools endorses the need for systemic accountability, not only for compliance but also to identify unanticipated ethical consequences, while a checklist developed by Vollmer and colleagues can help journal editors and peer reviewers to critically evaluate health AI studies. Oversight through open-source communities and citizen science projects could also help to build confidence that AI tools are fit for purpose. Democratising the full health AI pathway through transparent communication is key to cultivating trust in public health strategies, and a participatory approach is an excellent opportunity for historically under-represented voices to be heard. But stringent policies and legal reforms are needed from independent government bodies and regulatory agencies to support equitable deployment at scale, and to provide oversight and accountability for negative consequences.

The level of education, training of researchers and technologists and international scientific, technological and technological development allows the world community to gradually move to the formation and management of the industry 5.0 through the cognitive technological mind with the help of ensembles of intelligent agents and digital twins and automated systems with artificial intelligence. The cognitive technological mind on the speech mission of a person forms a design implementation for ensembles of intellectual agents. The ensembles of intelligent agents for the project implementation of a human task activate the work of digital twins with artificial intelligence, corresponding to the industry, for the production implementation of a human task. Cognitive technological intelligence, intelligent agent ensembles, and digital doubles make decisions based on representative data. Their representativeness is reflected by the similarity of the data in terms of the qualitative properties and characteristics of a certain production. Through cognitive technological mind with the help of ensembles of intellectual agents it is also possible to manage financial, ecological and other activities of enterprises of industry 5.0. Cognitive technological mind, ensembles of intellectual agents and digital doubles are a very effective tool of automation of management in all spheres of life of society and the state. The international scientific engineering community can begin the formation and technical implementation of industry control automation systems 5.0 based on cognitive virtual mind with artificial intelligence, additive technologies and 5G network. Sensitive safe robots with cognitive artificial mind will perform high-tech professional work in various fields of industry 5.0. A person will teach robots with a cognitive mind to increase professionalism by recursive self-improvement to technological singularity. Artificial intelligence with technological singularity will be controlled by the results of self-improvement in simulation mode in virtual space. He can become a good assistant to humanity in the safe development of living spaces.

Purpose. The study aims to understand artificial intelligence as a socio-cultural phenomenon and its impact on education, where the spiritual sphere of humanity, moral norms, values, and human cognitive abilities are preserved, transferred as well as reproduced. A new discourse on the interaction of artificial and authentic human intelligence becomes inevitable, which has led to a situation of uncertainty. Changes in the socio-cultural environment under the influence of artificial intelligence increase potential threats to the educational space, which stimulates to find the ways to eliminate them. Theoretical basis. Various approaches of classical and postmodern philosophical heritage were taken as a theoretical basis for the research. The originality of the study is in the interpretation of artificial intelligence as a modern form of alienation of essential human characteristics in the socio-cultural context of information technology. The expansion of artificial intelligence raises awareness of the existential threat to the basic socio-cultural, moral and ethical principles of humanism. It is proved that various forms of alienation in the current existing socio-cultural space are typical of our reality, which changes the system of values, moral principles, and social organization of the community. Conclusions. In conclusion, it is proved that AI is a natural stage of scientific and technological progress, which reflects its secondary, derivative nature from human (authentic) intelligence. Human intelligence will always have advantages over AI due to its ability to create, communicate socially and culturally, and be emotional. The dilemma of the counterbalance between human and artificial intelligence is perceived mainly at the emotional level of people. The millennial understanding of the primacy of the creator over his creation can traditionally overcome this contradiction. The universality of human thinking is an undeniable advantage of human intelligence and a guarantee of its, i.e. our, priority.

The study investigates the evolving linguistic dynamics between humans and artificial intelligence (AI) by asking the central question: Who feeds whom? The research examines two key data sources through a discourse-analytic approach: real world human–AI interactions and AI-related discourse in public and corporate domains. Six recurring themes emerged: AI as Helper/Assistant, Human as Data Feeder, AI as Expert/Advisor, AI Anthropomorphized, Ambiguous Agency, and Human Deference to AI. This revealed a complex, bidirectional relationship shaped through language. While corporate discourse positions humans as data providers who “feed” and “train” AI, user interactions suggest a reversal, wherein AI increasingly supplies language, advice, and authority that users readily adopt. This linguistic inversion challenges traditional notions of control and authorship. The study also identifies a troubling ambiguity around agency, with users and developers attributing quasi-human traits and decision-making capacity to AI systems. Anthropomorphism and discursive deference further entrench AI as a credible, even empathetic, interlocutor. Language emerges as the mirror and engine of this relationship, shaping user expectations, reinforcing power dynamics, and influencing communicative norms. The findings highlight the urgent need for discursive transparency in AI design, critical literacy in user education, and expanded ethical frameworks that account for language as a form of soft power. Ultimately, this inquiry argues that understanding human–AI relationships requires more than technical literacy; it demands critical awareness of the linguistic structures through which these relationships are constructed, negotiated, and sustained. The metaphorical question—Who is feeding whom?—invites ongoing reflection on power, trust, and authorship in the age of AI.

Artificial Intelligence has made possible the latest revolutions in the industry. Nevertheless, adversarial AI turns out to be a serious challenge because of its tendency to exploit the vulnerabilities of machine learning models, breach their security, and eventually lead them to fail, mostly unless very few. Adversarial attacks can be evasion and poisoning, model inversion, and so forth; they indeed say how fragile an AI system is and also suggest a proper immediate call for solid defensive structures. Several adversarial defense mechanisms have been proposed―from adversarial training to defensive distillation and certified defenses―yet they remain vulnerable to high-level attacks. This included the emergence of explainable artificial intelligence (XAI) as one of the significant components in AI security, whereby capturing interpretability and transparency can lead to better threat detection and user trust. This work encompasses a literature review of adversarial AIs, current developments in adversarial defenses, and the role played by XAI in reducing threats from such adversarial systems. In effect, the paper presents an integrated framework with techniques of explainability for the building of resilient, transparent, and trustworthy AI systems.

Tackling the grand challenges of 21st century agriculture (Ag) will require a fundamental shift in the way we envision the role of artificial intelligence (AI) technologies, and in the way we build agricultural AI systems. This shift is needed especially for complex, high-value agricultural ecosystems such as those in the Western U.S., where 300+ crops are grown. Farmers and policy makers in this region face variable profitability, major crop loss and poor crop quality owing to several challenges, including increased labor costs and shortages of skilled workers, weather and management uncertainties, and water scarcity. While AI is expected to be a significant tool for addressing these challenges, AI capabilities must be expanded and will need to account for human input and human behavior – calling for a strong AI-Ag coalition that also creates new opportunities to achieve sustained innovation. Accomplishing this goal goes well beyond the scope of any specific research project or disciplinary silo and requires a more holistic transdisciplinary effort in research, development, and training. To respond to this need, we initiated the AgAID Institute, a multi-institution, transdisciplinary National AI Research Institute that will build new public-private partnerships involving a diverse range of stakeholders in both agriculture and AI. The institute focuses its efforts on providing AI solutions to specialty crop agriculture where the challenges pertaining to water availability, climate variability and extreme weather, and labor shortages, are all significantly pronounced. Our approach to all AgAID Institute activities is being guided by three cross-cutting principles: (i) adoption as a first principle in AI design; (ii) adaptability to changing environments and scales, and (iii) amplification of human skills and machine efficiency. The AgAID Institute is conducting a range of activities including: using agricultural AI applications as testbeds for developing innovative AI technologies and workflows; laying the technological foundations for climate-smart agriculture; serving as a nexus for culturally inclusive collaborative and transdisciplinary learning and knowledge co-production; preparing the next generation workforce for careers at the intersection of Ag and AI technology; and facilitating technology adoption and transfer.

With the continuous development and maturity of artificial intelligence technology, a new round of technological and industrial change is rapidly emerging, profoundly affecting all aspects of human life, China is also rapidly entering the era of intelligence. In recent years, artificial intelligence has been widely applied in art design. Artificial intelligence technology has been extended to different types of art design fields. The main role of artificial intelligence in art design is assistant design. The purpose of this paper is to find out the shortcomings of artificial intelligence in the application of art design through the research on artificial-intelligence-based auxiliary methods of art design, so as to help realize the deep cross-border integration between artificial intelligence and art design. This paper first summarizes the concept and relationship between artificial intelligence and art design, and then briefly analyzes the role of auxiliary design. Then, the main problems of the current auxiliary methods are analyzed with the help of the intelligent algorithm. Finally, the improvement strategies of the auxiliary methods are proposed to solve these problems. The experiments in this paper show that the existing auxiliary design methods are general and have some problems, which hinder the deep crossover between artificial intelligence and artistic design.

Artificial intelligence (AI) is rapidly advancing and holds both opportunities and risks for the social work profession. This review article provides an analysis of the potential applications and implications of AI technologies for social work practice, education, research, and ethics. The article highlights promising uses of AI tools like predictive risk models, virtual simulations, chatbots, and data analytics to enhance social work services and training. However, it also critically examines the ethical considerations and social justice concerns related to the integration of AI in social work, including issues of privacy, transparency, accountability, consent, bias, displacement of roles, and threats to human dignity. To responsibly leverage AI's benefits while mitigating its risks, the article offers recommendations for social workers to collaborate on AI design, follow ethical protocols, pursue education on AI, and conduct further research. Key areas for future research identified include studying client perspectives on AI, testing the efficacy of AI systems for social work tasks, auditing algorithms for bias, and analyzing AI's impact on social work employment. In conclusion, while acknowledging AI's significant potential, the article emphasizes that purposeful oversight, participatory design, and adherence to social work values must remain priorities to ensure AI promotes dignity, autonomy, and justice. This review provides a balanced, evidence-based analysis to inform discussions on optimizing AI's integration in the social work profession.

This research comprehensively explores the interplay between artificial intelligence (AI) crime and cybersecurity. The study aims to perform a criminological analysis to understand AI’s impacts on cybersecurity, highlighting its benefits and potential risks. A significant aspect of this research is investigating liability issues associated with AI’s deployment. These concerns are not limited to specific applications, such as self-driving vehicles, but extend across various AI-utilizing sectors. AI’s capability to autonomously make decisions, sometimes with severe implications for individuals, poses the critical question of responsibility. When AI-driven decisions lead to adverse outcomes, the dilemma arises: who should be held accountable - the developers, the users, or the AI itself? Another vital research question examines how AI influences cybercrime. This study scrutinises AI’s role in transforming cybercrime’s nature and the new security risks it introduces. It questions the adequacy of current criminal laws in addressing novel crime forms emerging from AI advancements and explores how these laws might need to evolve. Moreover, the research investigates AI’s role in amplifying or simplifying traditional crimes, such as through the creation of phishing programs or the use of DeepFake in identity theft. The impact of AI on digital evidence forms another critical area of investigation. AI algorithms, capable of efficiently analysing vast data sets, can significantly aid in crime detection and perpetrator identification. However, this advancement also raises concerns about the authenticity of digital evidence. Technologies like deepfake, capable of producing convincing fake images and videos, present a formidable challenge in distinguishing real from fabricated evidence, especially in legal contexts. Lastly, the research delves into AI’s potential in crime prevention. It assesses how AI-driven predictive models can identify likely crime hotspots and timings, enabling more effective resource allocation by police and security services. The study also explores advancements in AI’s facial and object recognition technologies, highlighting their potential in criminal identification. In summary, this research offers a detailed examination of AI’s multifaceted impact on cybersecurity, liability issues, cybercrime, digital evidence, and crime prevention, presenting a nuanced understanding of AI’s challenges and opportunities in law enforcement and legal accountability.

Global healthcare organisations are using artificial intelligence (AI) to improve cross-cultural understanding and diversity in patient care. AI helps with language translation, making communication, diagnosis and treatment more effective. AI also creates care plans that consider patients’ cultural preferences and assign culturally appropriate caregivers, enhancing care quality. However, biases in AI models are a concern, and involving minorities in AI design is important to reduce these biases. AI has great potential in healthcare, especially for underprivileged populations, by improving communication and reducing prejudices. Challenges like data biases, though, need to be addressed to prevent increasing inequalities. Strong regulations and diverse AI features are necessary to ensure fair and effective healthcare. Overall, AI can improve patient care by considering cultural diversity, but careful implementation is needed to avoid inequalities.

English speaking with artificial intelligence (AI): The roles of enjoyment, willingness to communicate with AI, and innovativeness

BackgroundDespite advancements in artificial intelligence (AI) to develop prediction and classification models, little research has been devoted to real-world translations with a user-centered design approach. AI development studies in the health care context have often ignored two critical factors of ecological validity and human cognition, creating challenges at the interface with clinicians and the clinical environment.ObjectiveThe aim of this literature review was to investigate the contributions made by major human factors communities in health care AI applications. This review also discusses emerging research gaps, and provides future research directions to facilitate a safer and user-centered integration of AI into the clinical workflow.MethodsWe performed an extensive mapping review to capture all relevant articles published within the last 10 years in the major human factors journals and conference proceedings listed in the “Human Factors and Ergonomics” category of the Scopus Master List. In each published volume, we searched for studies reporting qualitative or quantitative findings in the context of AI in health care. Studies are discussed based on the key principles such as evaluating workload, usability, trust in technology, perception, and user-centered design.ResultsForty-eight articles were included in the final review. Most of the studies emphasized user perception, the usability of AI-based devices or technologies, cognitive workload, and user’s trust in AI. The review revealed a nascent but growing body of literature focusing on augmenting health care AI; however, little effort has been made to ensure ecological validity with user-centered design approaches. Moreover, few studies (n=5 against clinical/baseline standards, n=5 against clinicians) compared their AI models against a standard measure.ConclusionsHuman factors researchers should actively be part of efforts in AI design and implementation, as well as dynamic assessments of AI systems’ effects on interaction, workflow, and patient outcomes. An AI system is part of a greater sociotechnical system. Investigators with human factors and ergonomics expertise are essential when defining the dynamic interaction of AI within each element, process, and result of the work system.

BackgroundDespite the recognition that developing artificial intelligence (AI) that is trustworthy is necessary for public acceptability and the successful implementation of AI in healthcare contexts, perspectives from key stakeholders are often absent from discourse on the ethical design, development, and deployment of AI. This study explores the perspectives of birth parents and mothers on the introduction of AI-based cardiotocography (CTG) in the context of intrapartum care, focusing on issues pertaining to trust and trustworthiness.MethodsSeventeen semi-structured interviews were conducted with birth parents and mothers based on a speculative case study. Interviewees were based in England and were pregnant and/or had given birth in the last two years. Thematic analysis was used to analyze transcribed interviews with the use of NVivo. Major recurring themes acted as the basis for identifying the values most important to this population group for evaluating the trustworthiness of AI.ResultsThree themes pertaining to the perceived trustworthiness of AI emerged from interviews: (1) trustworthy AI-developing institutions, (2) trustworthy data from which AI is built, and (3) trustworthy decisions made with the assistance of AI. We found that birth parents and mothers trusted public institutions over private companies to develop AI, that they evaluated the trustworthiness of data by how representative it is of all population groups, and that they perceived trustworthy decisions as being mediated by humans even when supported by AI.ConclusionsThe ethical values that underscore birth parents and mothers’ perceptions of trustworthy AI include fairness and reliability, as well as practices like patient-centered care, the promotion of publicly funded healthcare, holistic care, and personalized medicine. Ultimately, these are also the ethical values that people want to protect in the healthcare system. Therefore, trustworthy AI is best understood not as a list of design features but in relation to how it undermines or promotes the ethical values that matter most to its end users. An ethical commitment to these values when creating AI in healthcare contexts opens up new challenges and possibilities for the design and deployment of AI.

The research paper aims to study The Rise of Artificial Intelligence: A Boon or a Bane under Legal Regime. It will examine how the Artificial Intelligence (AI) in the real world refers in various domains and across various fields to boost innovation with transformative technology. The jurisprudential aspect highlights the philosophy of law concerning Artificial Intelligence along with its fundamental principles and moral values. It will analyse AI as a catalyst for progressing in today’s current era which symbolises the capacity of human intellect with AI through development in the modern day. AI as a positive footprint in the society understands the interaction of humans and AI. It will also examine the AI as a loss of human touch and its values where there is a gap between emotional connection between AI and human. The growing privacy challenges with regard to breach of personal data is a threat to human world. It will analyse the ethical and legal concerns of AI systems with lack of privacy and data protection. The growing dependence of AI poses challenges in security concerns. It will understand the AI in legal profession while providing legal services. It contrasts AI with major countries like The United States, The United Kingdom and The European Union as a comparative analysis. It will examine that how AI is truly a boon or a bane.

This article examines the evolving relationship between creativity and artificial intelligence in design by analysing how artificial intelligence tools influence creative work within designer-led processes across design domains. A semi-systematic literature review of 67 studies maps artificial intelligence contributions to idea generation, decision-making, and conceptual exploration across stages of the design process. Findings inform a functional taxonomy grouping tools by creative support roles, stimulus generation, optimisation, evaluation, and hybrid workflow support, prioritising situated use over technological typologies. Recurring challenges include dependence on automated outputs, interface-driven shifts in ideation strategies and reduced diversity of exploration. The taxonomy enables assessment of how artificial intelligence contributes to ideation and refinement, informing research, education, and professional practice concerned with exploratory breadth, diversity, and workflow efficiency.