The achievement of the 2030 Sustainable Development Goals (SDGs) is dependent upon strategic resource distribution. We propose a causal discovery framework using Panel Vector Autoregression, along with both country-specific fixed effects and PCMCI+ conditional independence testing on 168 countries (2000-2025) to develop the first complete causal architecture of SDG dependencies. Utilizing 8 strategically chosen SDGs, we identify a distributed causal network (i.e., no single 'hub' SDG), with 10 statistically significant Granger-causal relationships identified as 11 unique direct effects. Education to Inequality is identified as the most statistically significant direct relationship (r = -0.599; p < 0.05), while effect magnitude significantly varies depending on income levels (e.g., high-income: r = -0.65; lower-middle-income: r = -0.06; non-significant). We also reject the idea that there exists a single 'keystone' SDG. Additionally, we offer a proposed tiered priority framework for the SDGs namely, identifying upstream drivers (Education, Growth), enabling goals (Institutions, Energy), and downstream outcomes (Poverty, Health). Therefore, we conclude that effective SDG acceleration can be accomplished through coordinated multi-dimensional intervention(s), and that single-goal sequential strategies are insufficient.

Many recent studies have investigated social biases in LLMs but brand bias has received little attention. This research examines the biases exhibited by LLMs towards different brands, a significant concern given the widespread use of LLMs in affected use cases such as product recommendation and market analysis. Biased models may perpetuate societal inequalities, unfairly favoring established global brands while marginalizing local ones. Using a curated dataset across four brand categories, we probe the behavior of LLMs in this space. We find a consistent pattern of bias in this space -- both in terms of disproportionately associating global brands with positive attributes and disproportionately recommending luxury gifts for individuals in high-income countries. We also find LLMs are subject to country-of-origin effects which may boost local brand preference in LLM outputs in specific contexts.

Prior research demonstrates that Open AI's GPT models can predict variations in moral opinions between countries but that the accuracy tends to be substantially higher among high-income countries compared to low-income ones. This study aims to replicate previous findings and advance the research by examining how accuracy varies with different types of moral questions. Using responses from the World Value Survey and the European Value Study, covering 18 moral issues across 63 countries, we calculated country-level mean scores for each moral issue and compared them with GPT-4's predictions. Confirming previous findings, our results show that GPT-4 has greater predictive success in high-income than in low-income countries. However, our factor analysis reveals that GPT-4 bases its predictions primarily on a single dimension, presumably reflecting countries' degree of conservatism/liberalism. Conversely, the real-world moral landscape appears to be two-dimensional, differentiating between personal-sexual and violent-dishonest issues. When moral issues are categorized based on their moral domain, GPT-4's predictions are found to be remarkably accurate in the personal-sexual domain, across both high-income (r = .77) and low-income (r = .58) countries. Yet the predictive accuracy significantly drops in the violent-dishonest domain for both high-income (r = .30) and low-income (r = -.16) countries, indicating that GPT-4's one-dimensional world-view does not fully capture the complexity of the moral landscape. In sum, this study underscores the importance of not only considering country-specific characteristics to understand GPT-4's moral understanding, but also the characteristics of the moral issues at hand.

Biases in AI tools such as those used to detect signs of disease could exacerbate inequalities in health care.Credit:Jiraroj Praditcharoenkul/Alamy A lack of racial and gender diversity could be hindering the efforts of researchers working to improve the fairness of artificial intelligence (AI) tools in health care, such as those designed to detect disease from blood samples or imaging data. Scientists analysed 375 research and review articles on the fairness of artificial intelligence in health care, published in 296 journals between 1991 and 2022. Of 1,984 authors, 64% were white, whereas 27% were Asian, 5% were Black and 4% were Hispanic (see'Gaps in representation'). The analysis, published as a preprint on medRxiv1, also found that 60% of authors were male and 40% female, a gender gap that was heightened among last authors, who often have a senior role in leading the research. "These findings are a reflection of what's happening in the research community at large," says study co-author Leo Anthony Celi, a health informatician and clinical researcher at the Massachusetts Institute of Technology in Boston.

In a recent study published in PLOS Digital Health, researchers reviewed existing literature on the use of artificial intelligence (AI) in health care to characterize the AI applications used in the clinical applications during the coronavirus disease 2019 (COVID-19) pandemic, investigate the location, timing, and extent of AI use in healthcare, and examine the United States (U.S.) regulatory approval processes. Despite the large number of approvals granted by the U.S. Food and Drug Administration (FDA) to AI applications in healthcare in the last six years, the adoption of AI applications in different areas of healthcare has been limited. Furthermore, there is limited information on the development and use of AI applications during the COVID-19 pandemic, unlike the significant and rapid growth in telehealth and vaccine technologies. While previous reviews have reviewed the potential uses, challenges, and impacts of AI applications for COVID-19 clinical response, many of the reviews found methodological flaws and potential biases in the use of AI applications in clinical practice. A scarcity of reviews provides a comprehensive report on the development, testing, and applications of AI in COVID-19 clinical responses.

The idea of "reinventing the wheel" is very often looked down upon in research. But many devices and solutions in the assistive technology (AT) space have been available for nearly half a century and still have not reached most users in low-income countries. Two such examples are refreshable Braille displays, which make digital data accessible in Braille through touch rather than audio, and tactile diagrams, which are critical to helping visually impaired people to pursue subjects, such as science, where diagrams are crucial to understanding the concepts. While accessibility normally refers only to the modality for making information accessible, in the Indian context, it is tightly tied to affordability. No market exists in the AT space in low-income countries, though the need is very high, because the user's ability to pay is either low or non-existent.

Artificial intelligence has made a disheartening discovery – 85 percent of the world's population has already been affected by human-induced climate change. The findings were made by German scientists, led by Max Callaghan from the Mercator Research Institute on Global Commons and Climate Change, who, according to the study, trained the system to'identify, evaluate and summarize scientific publications on climate change and its consequences.' Researchers used machine learning to sift through data published from 1951 through 2018 and found more than 100,000 studies with evidence that shows 80 percent of Earth's inhabited land has been impacted by climate change. The results also uncovered an'attribution gap' around the globe, where evidence is is distributed unequally across countries - 'evidence for potentially attributable impacts are twice as prevalent in high-income than in low-income countries,' according to the study. Artificial intelligence has made a disheartening discovery – 85 percent of the world's population has already been affected by human-induced climate change.

As the world grapples with the devastation of the coronavirus, one thing is clear: The United States simply wasn't prepared. Despite repeated warnings from infectious disease experts over the years, we lacked essential beds, equipment, and medication; public health advice was confusing; and our leadership offered no clear direction while sidelining credible health professionals and institutions. Infectious disease experts agree that it's only a matter of time before the next pandemic hits, and that one could be even more deadly. So how do we fix what COVID-19 has shown was broken? In this Mother Jones series, we're asking experts from a wide range of disciplines one question: What are the most important steps we can take to make sure we're better prepared next time around? On a hazy day in early March, a drone packaged in protective red casing and carrying precious cargo descended upon a crowd gathered in the Ashanti region of Ghana.

Despite huge advancements and progress in the world of global health over the past decades, many middle and low-income countries are still falling behind, unable to reach their sustainable development goals. This, in turn, is creating an urgency to prioritize wellbeing, and AI holds enormous promise in transforming the provision of healthcare in resource strained environments. The Artificial Intelligence in Global Health report, funded by the USAID's Center for Innovation and Impact, Rockefeller Foundation, and the Bill & Melinda Gates Foundation, outlined 27 cases of AI in global healthcare, and the massive potential it holds for drastically improving health in LEDC's. The use of AI was split into four key areas - population health, patient and front line health worker virtual assistants, and physician clinical decision support. Not only does the report provide solutions that could improve the access, quality, and effectiveness of global healthcare, but it also takes into account the current maturity of AI systems and the feasibility of these solutions.

A team of researchers from Bioversity International in Africa has created a smartphone app to help banana farmers protect their crops against diseases and pests. The Tumaini App (meaning'hope' in Swahili) is based on artificial intelligence algorithms that have been trained to recognize five major diseases and one common pest affecting the world's favorite fruit, demonstrating accuracy of more than 90 per cent in most models. The software has been tested in Colombia, the Democratic Republic of the Congo, India, Benin, China, and Uganda. Tumaini can recommend the means of addressing a specific disease and automatically upload identification data into a global database to help coordinate international response. It is hoped that the app can stop disease outbreaks and protect the livelihood of small, independent farmers.