We present a training module in AI ethics designed to prepare a broad group of professionals to recognize and address potential ethical challenges of AI applications in healthcare. Training materials include a two-page checklist, a brief glossary, and three practical case studies. While we have developed and applied this framework for training Research Ethics Committee members in France and South Africa, it can also be helpful in university courses ranging from public health and healthcare law to biomedical engineering and applied ethics.

This study tests whether citizens’ evaluations of the performance of artificial intelligence (AI) in public policies are subject to motivated reasoning. Specifically, we test whether respondents’ preferences for AI regulation or their subjective attitudes toward AI are sources of motivated reasoning across varying use cases, differing in nature, complexity, safety-criticality and normative considerations: AI in municipal services, self-driving cars and recidivism prediction. Experimental results from two preregistered studies conducted among German citizens reveal that subjective attitudes toward AI cause substantial and robust motivated reasoning across all three policy domains. Regulatory preferences are only a selective source for motivated reasoning about AI in public policy. Overall, the results point to the cognitive limitations of strategies that attempt to objectify the benefits of AI without considering the context of the application domain. Politicians and policymakers need to consider these limitations in their attempts to increase citizens’ appreciation of AI in public policy.

With the increasing volume of scientific literature, there is a need to streamline the screening process for titles and abstracts in systematic reviews, reduce the workload for reviewers, and minimize errors. This study validated artificial intelligence (AI) tools, specifically Llama 3 70B via Groq’s application programming interface (API) and ChatGPT-4o mini via OpenAI’s API, for automating this process in biomedical research. It compared these AI tools with human reviewers using 1,081 articles after duplicate removal. Each AI model was tested in three configurations to assess sensitivity, specificity, predictive values, and likelihood ratios. The Llama 3 model’s LLA_2 configuration achieved 77.5% sensitivity and 91.4% specificity, with 90.2% accuracy, a positive predictive value (PPV) of 44.3%, and a negative predictive value (NPV) of 97.9%. The ChatGPT-4o mini model’s CHAT_2 configuration showed 56.2% sensitivity, 95.1% specificity, 92.0% accuracy, a PPV of 50.6%, and an NPV of 96.1%. Both models demonstrated strong specificity, with CHAT_2 having higher overall accuracy. Despite these promising results, manual validation remains necessary to address false positives and negatives, ensuring that no important studies are overlooked. This study suggests that AI can significantly enhance efficiency and accuracy in systematic reviews, potentially revolutionizing not only biomedical research but also other fields requiring extensive literature reviews.

This article challenges the dominant narrative of AI in Iran as a symbol of national success and technological sovereignty by examining its materiality. The Iranian government often underscores AI’s role in countering sanctions and securing national interests. However, this national narrative overlooks the complex realities of AI’s implementation. By examining the material endpoints of AI – such as data centers, supercomputers, and digital labor – this article reveals a fragmented vision of AI, one that is entangled with global neoliberal practices. The analysis uncovers the sociopolitical and economic forces shaping AI in Iran, arguing that it reflects both the nation’s ambitions and its vulnerabilities, offering a nuanced perspective on AI and its role in contemporary Iranian society.

The EU AI Act seeks to balance the need for societal protection against the potential risks of AI systems, with the goal of fostering innovation. However, the Act’s ex-ante risk-based approach might lead to regulatory obsolescence (already materialised in 2021 with the spread of LLMs and the consequent reopening of the regulatory process), as well as to over or under-inclusion of AI applications in risks’ categories. The paper deals with the latter outcome by exploring how AI uses in law and rulemaking hide risks not covered by the EU AI Act. It is then analysed as to how the Act lacks flexibility on amending its provisions, and the way forward. The latter is tackled without utopian and not really feasible proposals for a new act and risk-based approach, but focusing on codes of conduct and national interventions on AI uses by public authorities.

Systematic reviews are essential for evidence-based health care, but conducting them is time- and resource-consuming. To date, efforts have been made to accelerate and (semi-)automate various steps of systematic reviews through the use of artificial intelligence (AI) and the emergence of large language models (LLMs) promises further opportunities. One crucial but complex task within systematic review conduct is assessing the risk of bias (RoB) of included studies. Therefore, the aim of this study was to test the LLM Claude 2 for RoB assessment of 100 randomized controlled trials, published in English language from 2013 onwards, using the revised Cochrane risk of bias tool (‘RoB 2’; involving judgements for five specific domains and an overall judgement). We assessed the agreement of RoB judgements by Claude with human judgements published in Cochrane reviews. The observed agreement between Claude and Cochrane authors ranged from 41% for the overall judgement to 71% for domain 4 (‘outcome measurement’). Cohen’s κ was lowest for domain 5 (‘selective reporting’; 0.10 (95% confidence interval (CI): −0.10–0.31)) and highest for domain 3 (‘missing data’; 0.31 (95% CI: 0.10–0.52)), indicating slight to fair agreement. Fair agreement was found for the overall judgement (Cohen’s κ: 0.22 (95% CI: 0.06–0.38)). Sensitivity analyses using alternative prompting techniques or the more recent version Claude 3 did not result in substantial changes. Currently, Claude’s RoB 2 judgements cannot replace human RoB assessment. However, the potential of LLMs to support RoB assessment should be further explored.

In today’s world, smart algorithms—artificial intelligence (AI) and other intelligent systems—are pivotal for promoting the development agenda. They offer novel support for decision-making across policy planning domains, such as analysing poverty alleviation funds and predicting mortality rates. To comprehensively assess their efficacy and implications in policy formulation, this paper conducts a systematic review of 207 publications. The analysis underscores their integration within and across stages of the policy planning cycle: problem diagnosis and goal articulation; resource and constraint identification; design of alternative solutions; outcome projection; and evaluation. However, disparities exist in smart algorithm applications across stages, economic development levels, and Sustainable Development Goals (SDGs). While these algorithms predominantly focus on resource identification (29%) and contribute significantly to designing alternatives—such as long-term national energy policies—and projecting outcomes, including predicting multi-scenario land-use ecological security strategies, their application in evaluation remains limited (10%). Additionally, low-income nations have yet to fully harness AI’s potential, while upper-middle-income countries effectively leverage it. Notably, smart algorithm applications for SDGs also exhibit unevenness, with more emphasis on SDG 11 than on SDG 5 and SDG 17. Our study identifies literature gaps. Firstly, despite theoretical shifts, a disparity persists between physical and socioeconomic/environmental planning applications. Secondly, there is limited attention to policy-making in development initiatives, which is critical for improving lives. Future research should prioritise developing adaptive planning systems using emerging powerful algorithms to address uncertainty and complex environments. Ensuring algorithmic transparency, human-centered approaches, and responsible AI are crucial for AI accountability, trust, and credibility.

This study compares the design practices and performance of ChatGPT 4.0, a large language model (LLM), against graduate engineering students in a 48-h prototyping hackathon, based on a dataset comprising more than 100 prototypes. The LLM participated by instructing two participants who executed its instructions and provided objective feedback, generated ideas autonomously and made all design decisions without human intervention. The LLM exhibited similar prototyping practices to human participants and finished second among six teams, successfully designing and providing building instructions for functional prototypes. The LLM’s concept generation capabilities were particularly strong. However, the LLM prematurely abandoned promising concepts when facing minor difficulties, added unnecessary complexity to designs, and experienced design fixation. Communication between the LLM and participants was challenging due to vague or unclear descriptions, and the LLM had difficulty maintaining continuity and relevance in answers. Based on these findings, six recommendations for implementing an LLM like ChatGPT in the design process are proposed, including leveraging it for ideation, ensuring human oversight for key decisions, implementing iterative feedback loops, prompting it to consider alternatives, and assigning specific and manageable tasks at a subsystem level.

Much work has been produced on the passport and on exceptional regimes of mobility, notably asylum or permanent immigration. There has, by contrast, been less research on that intermediary, ubiquitous legal device that is the visa, a fundamental manifestation of state discretion, a major source of arbitrariness, and a constant assertion of power over moving bodies. The visa is a manifestation of and is embedded in a deeper infrastructure of limited global mobility. Adopting a global and comparative perspective, this Article seeks to analyze the politics of the visa as the indispensable sesame of international travel but also as the ultimate manifestation of that infrastructure’s arbitrariness. The Article highlights an ideal type of the visa especially as it is deployed towards “at risk” categories, emphasizing the sort of performance it requires from applicants in adapting to the system’s demands as they are relayed by officials, corporations and, increasingly, algorithms and artificial intelligence. It will highlight the constraints of applying for the visa, its temporal frames, its assorted conditions, and its denial and possibilities of contestation thereof. The Article contends that, globally, the visa is the key legal infrastructure to channel systemic discrimination, frustrate professional and cultural opportunities, and keep families apart in ways that entrench class and racial divides.

Since the publication of “What is the Current and Future Status of Digital Mental Health Interventions?” the exponential growth and widespread adoption of ChatGPT have underscored the importance of reassessing its utility in digital mental health interventions. This review critically examined the potential of ChatGPT, particularly focusing on its application within clinical psychology settings as the technology has continued evolving through 2023 and 2024. Alongside this, our literature review spanned US Medical Licensing Examination (USMLE) validations, assessments of the capacity to interpret human emotions, analyses concerning the identification of depression and its determinants at treatment initiation, and reported our findings. Our review evaluated the capabilities of GPT-3.5 and GPT-4.0 separately in clinical psychology settings, highlighting the potential of conversational AI to overcome traditional barriers such as stigma and accessibility in mental health treatment. Each model displayed different levels of proficiency, indicating a promising yet cautious pathway for integrating AI into mental health practices.

As data becomes a key component of urban governance, the night-time economy is still barely visible in datasets or in policies to improve urban life. In the last 20 years, over 50 cities worldwide appointed night mayors and governance mechanisms to tackle conflicts, foster innovation, and help the night-time economy sector grow. However, the intersection of data, digital rights, and 24-hour cities still needs more studies, examples, and policies. Here, the key argument is that the increasing importance of the urban night in academia and local governments claims for much-needed responsible data practices to support and protect nightlife ecosystems. By understanding these ecosystems and addressing data invisibilities, it is possible to develop a robust framework anchored in safeguarding human rights in the digital space and create comprehensive policies to help such ecosystems thrive. Night-time governance matters for the data policy community for three reasons. First, it brings together issues covered in different disciplines by various stakeholders. We need to build bridges between sectors to avoid siloed views of urban data governance. Second, thinking about data in cities also means considering the social, economic, and cultural impact of datafication and artificial intelligence on a 24-hour cycle. Creating a digital rights framework for the night means putting into practice principles of justice, ethics, and responsibility. Third, as Night Studies is an emerging field of research, policy and advocacy, there is an opportunity to help shape how, why, and when data about the night is collected and made available to society.