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Both readings

The development of cyber threats related to the use of AI

The rapid development of artificial intelligence (AI) means that its role in cyberspace is also growing, both in terms of threats and defence against them. AI supports the automation of anomaly detection, data analysis, and incident response, which enhances protection efficiency. However, cybercriminals use AI-based solutions to create sophisticated attack tools, such as advanced phishing schemes, deepfakes, and hard-to-detect malware. The author analyses the role of AI in generating cyber threats and evaluates…

Przegląd Bezpieczeństwa Wewnętrznego · Crime

Evidence-backed problem

"That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformation

As generative AI is increasingly contributing to the spread of deceptively realistic misinformation, lawmakers have introduced regulations requiring the disclosure of AI-generated content. However, it is unclear if labels reduce the risk of users falling for AI-generated misinformation. To address this research gap, we study the effect of labels on users’ perception and the implications of mislabeling, focusing on AI-generated images. We first explored users’ opinions and expectations of labels using five focus…

Policy

Both readings

How Generative AI Empowers Attackers and Defenders Across the Trust & Safety Landscape

Generative AI (GenAI) is a powerful technology poised to reshape Trust & Safety. While misuse by attackers is a growing concern, its defensive capacity remains underexplored. This paper examines these effects through a qualitative study with 43 Trust & Safety experts across five domains: child safety, election integrity, hate and harassment, scams, and violent extremism. Our findings characterize a landscape in which GenAI empowers both attackers and defenders. GenAI dramatically increases the scale and speed of…

Crime

Evidence-backed problem

The impact of AI on the labour market

This study explores the impact of artificial intelligence (AI) on the labour market, focusing on changes in job roles, skill requirements, and human resource (HR) practices. Unlike previous surveys that primarily addressed technological aspects, this research systematically integrates technological, organisational, and institutional perspectives. The literature review (2020–2025) shows that AI adoption is closely associated with rising demand for technical and interdisciplinary skills, restructuring of work role…

Labor

Evidence-backed problem

Skill-biased technological change in the age of AI: a theoretical analysis of automation and inequality

This paper develops a general equilibrium model to analyze how artificial intelligence (AI)–driven automation reshapes productivity, labor markets, and income distribution. The model features heterogeneous workers, endogenous automation decisions, and irreversible skill investment choices, allowing a unified examination of displacement, complementarity, and skill-supply responses. Automation substitutes for labor in routine tasks, reducing demand and wages for low-skilled workers, while simultaneously enhancing…

Labor

Evidence-backed problem

Navigating ethical, regulatory, and implementation barriers to AI in healthcare: pathways toward inclusive digital health in low-resource settings—a scoping review

Background: Artificial intelligence (AI) has the potential to revolutionize healthcare delivery in low- and middle-income countries (LMICs), yet its rapid adoption raises complex ethical, regulatory, and implementation challenges. This review investigates these barriers and identifies emerging strategies that support equitable and inclusive AI deployment in resource-limited settings. Methods: Following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines, a systematic mapping of literature was conduc…

Policy

Evidence-backed gain

A generative artificial intelligence approach for peptide antibiotic optimization

Abstract Antibiotic resistance is rising globally, demanding faster, more reliable routes to design antimicrobial candidates. Although artificial-intelligence-based methods have accelerated antimicrobial discovery, most are designed to screen fixed libraries or generate candidates broadly, rather than optimize existing peptide scaffolds under practical design constraints. Here, to address this challenge, we present APEX generative optimization (ApexGO). ApexGO uses a transformer variational autoencoder that embe…

Science

AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases

Systemic vascular and neurodegenerative disorders are important causes of disability and death worldwide, mainly because of the late stage of diagnosis and the high cost of current screening tools. Artificial intelligence (AI) and multimodal retinal imaging offer a non-invasive and viable approach for early risk stratification and longitudinal monitoring. This review highlights how changes in the retinal vasculature and nerve layers are markers of underlying pathophysiologies related to cardiovascular, metabolic…

Health

AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases

Evolving surgical teams in the age of artificial intelligence and robotics

Surgery is a critical function of the healthcare system, key to addressing a substantial portion of the global disease burden. The integration of advanced artificial intelligence (AI) and robotics ecosystems into the operating room (OR) promises to radically transform surgery, with profound implications. This article analyzes the current state of surgical AI and robotic systems; presents a vision for their future, highlighting technological and research challenges and their associated impact on surgical teams; a…

Health

Evolving surgical teams in the age of artificial intelligence and robotics

Manipulation and Deception in Generative AI-Mediated Education: Preserving Epistemic Agency, Critical Thinking, and Creativity

Abstract Generative AI now mediates core parts of learning, yet we lack criteria to tell its legitimate pedagogical uses from manipulative and deceptive ones. We also know too little about how AI reshapes the growth of critical thinking and creativity, or about whether it accelerates drift from educational goods to evaluative metrics. Using a postdigital, pragmatist lens that treats classrooms as sociomaterial assemblages of people, platforms, and institutions, we propose three orienting principles: moral legiti…

Education

From AI adoption to AI governance: Developing a Buddhist interpretive framework for higher education

Artificial intelligence is increasingly being adopted in higher education to support teaching, learning, administration, quality assurance, and institutional planning. However, much of the current discussion remains focused on adoption, efficiency, and technological capability, with less attention to the interpretive and governance conditions required for responsible institutional use. This article addresses that gap by developing a Buddhist interpretive framework for AI governance in higher education. Drawing o…

Policy

From AI adoption to AI governance: Developing a Buddhist interpretive framework for higher education

Human-AI co-creation or conflict? Mapping art students’ diverse perspectives on creative identity with genAI: A Q methodology study

Abstract Generative AI (GenAI) is rapidly transforming creative fields, raising critical questions about its impact on artistic identity and authorship, which also influence art education. While studies have explored GenAI adoption, the diversity of art students’ subjective perspectives on collaborating with these tools remains under-examined. This study uses Q methodology, a mixed-methods approach to investigating subjectivity, to explore the distinct perspectives art students hold regarding their creative iden…

Education

Human-AI co-creation or conflict? Mapping art students’ diverse perspectives on creative identity with genAI: A Q methodology study

Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems

Background: National electronic health record (EHR) networks can support learning health systems (LHSs) by enabling large-scale data aggregation, monitoring, and benchmarking, but their capacity to produce trustworthy and locally deployable machine learning and artificial intelligence (ML/AI) models remains uncertain. We characterized major US national EHR networks and examined barriers to ML/AI development and deployment across the LHS cycle. Methods: We conducted an environmental scan combining PubMed searches…

Health

Machine learning applications in sport: a scoping review

Machine learning (ML) applications continue to grow in popularity across the sport industry, offering new opportunities for performance enhancement, injury prevention, and decision-making. The present scoping review examined the landscape of ML applications in sport by analyzing 270 peer-reviewed studies published between 2002 and 2024. ML was applied across 12 broad subject areas, with computer science, biomechanics, and sport psychology emerging as the most common domains of application. Key applications inclu…

Sports

Machine learning applications in sport: a scoping review

Determinants of Artificial Intelligence Adoption in Public Sector Human Resource Management: Empirical Evidence from Kazakhstan

As governments worldwide seek to modernise public administration through digital technologies, understanding the drivers and barriers of Artificial Intelligence (AI) adoption in Human Resource Management (HRM) becomes critically important. This paper investigates determinants of AI adoption among civil servants in Kazakhstan using a largescale empirical survey of 12,562 public servants conducted in June 2025. We construct and validate composite indices of internal and external HR quality factors (Cronbach's α =…

Labor