This study investigates how large language models, in particular LLaMA 3.2-3B, construct narratives about Black and white women in short stories generated in Portuguese. From 2100 texts, we applied computational methods to group semantically similar stories, allowing a selection for qualitative analysis. Three main discursive representations emerge: social overcoming, ancestral mythification and subjective self-realization. The analysis uncovers how grammatically coherent, seemingly neutral texts materialize a crystallized, colo-nially structured framing of the female body, reinforcing historical inequalities. The study proposes an integrated approach, that combines machine learning techniques with qualitative, manual discourse analysis.

When developers of artificial intelligence (AI) products need to decide between profit and safety for the users, they likely choose profit. Untrustworthy AI technology must come packaged with tangible negative consequences. Here, we envisage those consequences as the loss of reputation caused by media coverage of their misdeeds, disseminated to the public. We explore whether media coverage has the potential to push AI creators into the production of safe products, enabling widespread adoption of AI technology. We created artificial populations of self-interested creators and users and studied them through the lens of evolutionary game theory. Our results reveal that media is indeed able to foster cooperation between creators and users, but not always. Cooperation does not evolve if the quality of the information provided by the media is not reliable enough, or if the costs of either accessing media or ensuring safety are too high. By shaping public perception and holding developers accountable, media emerges as a powerful soft regulator -- guiding AI safety even in the absence of formal government oversight.

Recent studies indicate that robotic coaches can play a crucial role in promoting wellbeing. However, the real-world deployment of wellbeing robots raises numerous ethical and socio-technical questions and concerns. To explore these questions, we undertake a community-centered investigation to examine three different communities' perspectives on using robotic wellbeing coaches in real-world environments. We frame our work as an anticipatory ethical investigation, which we undertake to better inform the development of robotic technologies with communities' opinions, with the ultimate goal of aligning robot development with public interest. We conducted workshops with three communities who are under-represented in robotics development: 1) members of the public at a science festival, 2) women computer scientists at a conference, and 3) humanities researchers interested in history and philosophy of science. In the workshops, we collected qualitative data using the Social Robot Co-Design Canvas on Ethics. We analysed the collected qualitative data with Thematic Analysis, informed by notes taken during workshops. Through our analysis, we identify four themes regarding key ethical and socio-technical questions about the real-world use of wellbeing robots. We group participants' insights and discussions around these broad thematic questions, discuss them in light of state-of-the-art literature, and highlight areas for future investigation. Finally, we provide the four questions as a broad framework that roboticists can and should use during robotic development and deployment, in order to reflect on the ethics and socio-technical dimensions of their robotic applications, and to engage in dialogue with communities of robot users. The four questions are: 1) Is the robot safe and how can we know that?, 2) Who is the robot built for and with?, 3) Who owns the robot and the data?, and 4) Why a robot?.

Ongoing advancements in computer vision, particularly in pattern recognition and scene classification, have enabled new applications in environmental monitoring. Deep learning now offers non-contact methods for assessing water quality and detecting contamination, both critical for disaster response and public health protection. This work introduces HydroVision, a deep learning-based scene classification framework that estimates optically active water quality parameters including Chlorophyll-Alpha, Chlorophylls, Colored Dissolved Organic Matter (CDOM), Phycocyanins, Suspended Sediments, and Turbidity from standard Red-Green-Blue (RGB) images of surface water. HydroVision supports early detection of contamination trends and strengthens monitoring by regulatory agencies during external environmental stressors, industrial activities, and force majeure events. The model is trained on more than 500,000 seasonally varied images collected from the United States Geological Survey Hydrologic Imagery Visualization and Information System between 2022 and 2024. This approach leverages widely available RGB imagery as a scalable, cost-effective alternative to traditional multispectral and hyperspectral remote sensing. Four state-of-the-art convolutional neural networks (VGG-16, ResNet50, MobileNetV2, DenseNet121) and a Vision Transformer are evaluated through transfer learning to identify the best-performing architecture. DenseNet121 achieves the highest validation performance, with an R2 score of 0.89 in predicting CDOM, demonstrating the framework's promise for real-world water quality monitoring across diverse conditions. While the current model is optimized for well-lit imagery, future work will focus on improving robustness under low-light and obstructed scenarios to expand its operational utility.

A judge ruled on Tuesday that Google would not be forced to sell its Chrome browser or the Android operating system, saving the tech giant from the most severe penalties sought by the US government. The same judge had ruled in favor of US prosecutors nearly a year ago, finding that Google built and maintained an illegal monopoly with its namesake search engine. Groups critical of Google's dominance in the internet search and online advertising industry are furious. They contend the judge missed an opportunity to enact meaningful change in an industry that has suffocated under the crushing weight of its heaviest player. Tech industry groups and investors, by contrast, are thrilled. Shares in Alphabet, Google's parent company, have risen 9% since Tuesday afternoon.

OpenAI previously announced that it was considering allowing teens to add a trusted emergency contact to their account. But the company did not outline concrete plans to add such a measure in its most recent blog post. "These steps are only the beginning. We will continue learning and strengthening our approach, guided by experts, with the goal of making ChatGPT as helpful as possible," the company said. This announcement comes a week after the parents of teenage boy who died by suicide sued OpenAI, alleging its ChatGPT helped their son Adam "explore suicide methods."

For almost a year, the future of the world's most popular web browser has been a question mark. After the United States declared Google an illegal monopoly in online search, federal prosecutors put forth a forced divestment of Chrome as one possible legal remedy. The case is now resolved, pending appeal, and Google won't have to sell Chrome. Instead, Google will have to provide search index data and amalgamated user metrics to at least some of its competitors. Judge Amit Mehta ruled that the government prosecutors couldn't prove that Google's dominance in the browser space--just under 70 percent of market share, at the time of writing--was essential to its illegal monopoly in search, as Ars Technica reports.

Despite decades of green certifications, better material sourcing, and the use of more sustainable materials, the built environment is still responsible for a third of global emissions worldwide. According to a 2024 UN report, the building sector has fallen "significantly behind on progress" toward becoming more sustainable. Changing the way we erect and operate buildings remains key to tackling climate change. London-based design and research nonprofit Material Cultures is exploring how tradition can be harnessed in new ways to repair the contemporary building system. As many other practitioners look to artificial intelligence and other high-tech approaches, Material Cultures is focusing on sustainability, and finding creative ways to turn local materials into new buildings.

"We have stringent standards for the content of listings on our platform", the spokesperson added. "We are conducting a thorough review and are strengthening our monitoring processes." It is not known for how long the image was used, or who the company was that was selling it on the Chinese company's website. Many online have speculated the image was created using artificial intelligence (AI) - but it remains unclear how the picture was made. In April, Luigi Mangione pleaded not guilty to all federal charges brought over the fatal shooting of Mr Thompson.

A Victorian lawyer has become the first in Australia to face professional sanctions for using artificial intelligence in a court case, being stripped of his ability to practise as a principal lawyer after AI generated false citations that he had failed to verify. Guardian Australia reported in October last year that in a 19 July 2024 hearing, the anonymous solicitor representing a husband in a dispute between a married couple provided the court with a list of prior cases that had been requested by Justice Amanda Humphreys in relation to an enforcement application in the case. When Humphreys returned to her chambers, she said in a ruling that neither herself nor her associates were able to identify the cases in the list. When the matter returned to court the lawyer confirmed that the list had been prepared using legal software that utilised AI. He acknowledged he did not verify the accuracy of the information before submitting it to the court.