Abstaining classifiers have the option to refrain from providing a prediction for instances that are difficult to classify. The abstention mechanism is designed to trade off the classifier's performance on the accepted data while ensuring a minimum number of predictions. In this setting, often fairness concerns arise when the abstention mechanism solely reduces errors for the majority groups of the data, resulting in increased performance differences across demographic groups. While there exist a bunch of methods that aim to reduce discrimination when abstaining, there is no mechanism that can do so in an explainable way. In this paper, we fill this gap by introducing Interpretable and Fair Abstaining Classifier IFAC, an algorithm that can reject predictions both based on their uncertainty and their unfairness. By rejecting possibly unfair predictions, our method reduces error and positive decision rate differences across demographic groups of the non-rejected data. Since the unfairness-based rejections are based on an interpretable-by-design method, i.e., rule-based fairness checks and situation testing, we create a transparent process that can empower human decision-makers to review the unfair predictions and make more just decisions for them. This explainable aspect is especially important in light of recent AI regulations, mandating that any high-risk decision task should be overseen by human experts to reduce discrimination risks.

Chipmaker Nvidia says it plans to build artificial intelligence servers worth as much as 500bn in the United States over the next four years with help from partners such as TSMC. Nvidia is the latest US tech firm to back a push by President Donald Trump's administration for local manufacturing. Monday's announcement includes the production of its Blackwell AI chips at TSMC's factory in Phoenix, Arizona, and supercomputer manufacturing plants in Texas by Foxconn and Wistron, which are expected to ramp up in 12 to 15 months. "Adding American manufacturing helps us better meet the incredible and growing demand for AI chips and supercomputers, strengthens our supply chain and boosts our resiliency," Nvidia CEO Jensen Huang said. "Manufacturing AI chips and supercomputers in the US will create hundreds of thousands of jobs in the coming decades," Nvidia said in a statement.

While President Donald Trump has revealed a tariff exemption for some electronics, video game consoles are not thought to be among them. Christopher Dring, who writes about the gaming industry in The Game Business newsletter, said while Sony did not directly mention tariffs in its decision, their "knock-on effect" could have an impact on pricing worldwide. "The US is the biggest market for video game consoles, and rather than simply increase prices there, it's possible the likes of PlayStation could increase pricing globally in an effort to protect, as best they can, the US market," he told the BBC. "Ultimately, the era when game consoles went down in price over time is certainly over." The price increase comes as Nintendo's launch of its rival Switch 2 console was marred by the impact of US tariffs on markets worldwide.

In the early hours of April 12, 1812, a crowd of men approached Rawfolds Mill, a four-story stone building on the banks of the River Spen, in West Yorkshire. This was Brontë country--a landscape of bleak moors, steep valleys, and small towns nestled in the hollows. The men, who'd assembled on the moors hours earlier, were armed with muskets, sticks, hatchets, and heavy blacksmith's hammers. When they reached the mill, those at the front broke windows to gain entry, and some fired shots into the darkened factory. But the mill's owner, William Cartwright, had been preparing for trouble.

This story was originally published by WIRED and is reproduced here as part of the Climate Desk collaboration. Last Tuesday, President Donald Trump held a press conference to announce the signing of executive orders intended to shape American energy policy in favor of one particular source: coal, the most carbon-intense fossil fuel. "I call it beautiful, clean coal," President Trump said while flanked by a crowd of miners at the White House. "I tell my people never use the word coal unless you put'beautiful, clean' before it." Trump has talked about saving coal, and coal jobs, for as long as he's been in politics.

At least 34 people were killed and another 117, including 11 children, were injured by a Russian missile attack on the northern Ukrainian city of Sumy, Ukraine's state emergency service said. This was the deadliest attack on Ukraine this year. The Ukrainian Air Force said its units intercepted and destroyed 43 of 55 Russian drones launched at Ukraine overnight. The attacks reportedly targeted the northern, southern and central areas of Ukraine. Russian forces captured the village of Yelyzavetivka in Ukraine's Donetsk region, Russia's Ministry of Defence said.

We study the derivative-informed learning of nonlinear operators between infinite-dimensional separable Hilbert spaces by neural networks. Such operators can arise from the solution of partial differential equations (PDEs), and are used in many simulation-based outer-loop tasks in science and engineering, such as PDE-constrained optimization, Bayesian inverse problems, and optimal experimental design. In these settings, the neural network approximations can be used as surrogate models to accelerate the solution of the outer-loop tasks. However, since outer-loop tasks in infinite dimensions often require knowledge of the underlying geometry, the approximation accuracy of the operator's derivatives can also significantly impact the performance of the surrogate model. Motivated by this, we analyze the approximation errors of neural operators in Sobolev norms over infinite-dimensional Gaussian input measures. We focus on the reduced basis neural operator (RBNO), which uses linear encoders and decoders defined on dominant input/output subspaces spanned by reduced sets of orthonormal bases. To this end, we study two methods for generating the bases; principal component analysis (PCA) and derivative-informed subspaces (DIS), which use the dominant eigenvectors of the covariance of the data or the derivatives as the reduced bases, respectively. We then derive bounds for errors arising from both the dimension reduction and the latent neural network approximation, including the sampling errors associated with the empirical estimation of the PCA/DIS. Our analysis is validated on numerical experiments with elliptic PDEs, where our results show that bases informed by the map (i.e., DIS or output PCA) yield accurate reconstructions and generalization errors for both the operator and its derivatives, while input PCA may underperform unless ranks and training sample sizes are sufficiently large.

There is general agreement that fostering trust and cooperation within the AI development ecosystem is essential to promote the adoption of trustworthy AI systems. By embedding Large Language Model (LLM) agents within an evolutionary game-theoretic framework, this paper investigates the complex interplay between AI developers, regulators and users, modelling their strategic choices under different regulatory scenarios. Evolutionary game theory (EGT) is used to quantitatively model the dilemmas faced by each actor, and LLMs provide additional degrees of complexity and nuances and enable repeated games and incorporation of personality traits. Our research identifies emerging behaviours of strategic AI agents, which tend to adopt more "pessimistic" (not trusting and defective) stances than pure game-theoretic agents. We observe that, in case of full trust by users, incentives are effective to promote effective regulation; however, conditional trust may deteriorate the "social pact". Establishing a virtuous feedback between users' trust and regulators' reputation thus appears to be key to nudge developers towards creating safe AI. However, the level at which this trust emerges may depend on the specific LLM used for testing. Our results thus provide guidance for AI regulation systems, and help predict the outcome of strategic LLM agents, should they be used to aid regulation itself.

The dissemination of online hate speech can have serious negative consequences for individuals, online communities, and entire societies. This and the large volume of hateful online content prompted both practitioners', i.e., in content moderation or law enforcement, and researchers' interest in machine learning models to automatically classify instances of hate speech. Whereas most scientific works address hate speech classification as a binary task, practice often requires a differentiation into sub-types, e.g., according to target, severity, or legality, which may overlap for individual content. Hence, researchers created datasets and machine learning models that approach hate speech classification in textual data as a multi-label problem. This work presents the first systematic and comprehensive survey of scientific literature on this emerging research landscape in English (N=46). We contribute with a concise overview of 28 datasets suited for training multi-label classification models that reveals significant heterogeneity regarding label-set, size, meta-concept, annotation process, and inter-annotator agreement. Our analysis of 24 publications proposing suitable classification models further establishes inconsistency in evaluation and a preference for architectures based on Bidirectional Encoder Representation from Transformers (BERT) and Recurrent Neural Networks (RNNs). We identify imbalanced training data, reliance on crowdsourcing platforms, small and sparse datasets, and missing methodological alignment as critical open issues and formulate ten recommendations for research.

We present AstroLLaVA, a vision language model for astronomy that enables interaction with astronomical imagery through natural dialogue. By fine-tuning the LLaVA model on a diverse dataset of $\sim$30k images with captions and question-answer pairs sourced from NASA's `Astronomy Picture of the Day', the European Southern Observatory, and the NASA/ESA Hubble Space Telescope, we create a model capable of answering open-ended questions about astronomical concepts depicted visually. Our two-stage fine-tuning process adapts the model to both image captioning and visual question answering in the astronomy domain. We demonstrate AstroLLaVA's performance on an astronomical visual question answering benchmark and release the model weights, code, and training set to encourage further open source work in this space. Finally, we suggest a roadmap towards general astronomical data alignment with pre-trained language models, and provide an open space for collaboration towards this end for interested researchers.