An independent review of the Sara Sharif case has identified multiple failings from agencies before her murder in Surrey in 2023, following two years of abuse. The child safeguarding practice review, published on Thursday, said there were clearly several points in Sara's life, in particular during the last few months, where different actions could and should have been taken by the authorities. The system failed to keep her safe, it added. Responding to the report, the Children's Commissioner said the case was a catalogue of missed opportunities, poor communication and ill-informed assumptions. The education secretary said there had been the glaring failures across all agencies.

A smartphone shows the official website of ChatGPT Atlas. A smartphone shows the official website of ChatGPT Atlas. "It'd be really nice to have a service that was sort of just observing your life and proactively helping you when you needed it," said OpenAI CEO Sam Altman in a recent Q&A about OpenAI's plans. This vision is at the heart of a new crop of AI browsers, notably OpenAI's ChatGPT Atlas and Perplexity's Comet. AI browsers differ from traditional browsers in at least two important ways.

AI Relationships Are on the Rise. Secret chatbot flings are creating new legal challenges for married couples when it comes to infidelity. Rebecca Palmer isn't a psychic, but as a divorce attorney she can often see what's coming next. For many people today, as AI saturates every aspect of life --from work to therapy--the allure of an AI romance is tantalizing. Chatbots are dependable, can provide emotional support, and, for the most part, will never pick a fight with you.

As industrial operations move to the state, residents find that their drinking water has been promised to companies. In 2019, Corpus Christi, Texas's eighth-largest city, moved forward with plans to build a desalination plant. The facility, which was expected to be completed by 2023, at a cost of a hundred and forty million dollars, would convert seawater into fresh water to be used by the area's many refineries and chemical plants. The former mayor called it "a pretty significant day in the life of our city." In anticipation of the plant's opening, the city committed to provide tens of millions of gallons of water per day to new industrial operations, including a plastics plant co-owned by ExxonMobil and the Saudi Basic Industries Corporation, a lithium refinery for Tesla batteries, and a "specialty chemicals" plant operated by Chemours.

Automated interpretability research aims to identify concepts encoded in neural network features to enhance human understanding of model behavior. Within the context of large language models (LLMs) for natural language processing (NLP), current automated neuron-level feature description methods face two key challenges: limited robustness and the assumption that each neuron encodes a single concept (monosemanticity), despite increasing evidence of polysemanticity. This assumption restricts the expressiveness of feature descriptions and limits their ability to capture the full range of behaviors encoded in model internals. To address this, we introduce Polysemantic FeatuRe Identification and Scoring Method (PRISM), a novel framework specifically designed to capture the complexity of features in LLMs. Unlike approaches that assign a single description per neuron, common in many automated interpretability methods in NLP, PRISM produces more nuanced descriptions that account for both monosemantic and polysemantic behavior. We apply PRISM to LLMs and, through extensive benchmarking against existing methods, demonstrate that our approach produces more accurate and faithful feature descriptions, improving both overall description quality (via a description score) and the ability to capture distinct concepts when polysemanticity is present (via a polysemanticity score).

This paper introduces a framework for representing information about entities that do not exist or may never exist, such as those involving fictional entities, blueprints, simulations, and future scenarios. Traditional approaches that introduce "dummy instances" or rely on modal logic are criticized, and a proposal is defended in which such cases are modeled using the intersections of actual types rather than specific non existent tokens. The paper positions itself within the Basic Formal Ontology and its realist commitments, emphasizing the importance of practical, implementable solutions over purely metaphysical or philosophical proposals, arguing that existing approaches to non existent entities either overcommit to metaphysical assumptions or introduce computational inefficiencies that hinder applications. By developing a structured ontology driven approach to unreal patterns, the paper aims to provide a useful and computationally viable means of handling references to hypothetical or non existent entities.

Motion prediction, recently popularized under the term world models, refers to anticipating the future states of agents or the future evolution of a scene, which is rooted in human cognition to bridge perception and decision-making, enabling us to anticipate, adapt, and act within an ever-changing world. It lies at the core of intelligent autonomous systems, such as robotics and self-driving cars, to safely operate in dynamic and human-robot-mixed environments, and also informs broader time-series challenges. With advances in methods, representations, and datasets, the field has seen rapid progress, reflected in rapidly updated benchmark performance. However, when state-of-the-art methods are deployed in the real world, they are often found to struggle to generalize to open-world settings and fall short of deployment standards. This reveals a gap between reality and benchmarks, which are often idealized or ill-posed, and fail to capture real-world complexity. To address the pressing need for problem settings that better reflect real-world challenges and guide future research, this paper focuses on revisiting the generalization and applicability of motion prediction models, with an emphasis on robotics, autonomous driving, and human motion applications. We first provide a comprehensive taxonomy of motion prediction methods, covering representations, modelling methods, application domains, and evaluation protocols. We then revisit two fundamental problems: 1) how to push motion prediction models to be deployable to realistic deployment standards, where motion prediction does not act in a vacuum, but functions as one module of closed-loop autonomy stacks - it takes input from the localization and perception, and informs downstream planning and control.

The complexity and opacity of neural networks (NNs) pose significant challenges, particularly in high-stakes fields such as healthcare, finance, and law, where understanding decision-making processes is crucial. To address these issues, the field of explainable artificial intelligence (XAI) has developed various methods aimed at clarifying AI decision-making, thereby facilitating appropriate trust and validating the fairness of outcomes. Among these methods, prototype-based explanations offer a promising approach that uses representative examples to elucidate model behavior. However, a critical gap exists regarding standardized benchmarks to objectively compare prototype-based XAI methods, especially in the context of time series data. This lack of reliable benchmarks results in subjective evaluations, hindering progress in the field. We aim to establish a robust framework, ProtoScore, for assessing prototype-based XAI methods across different data types with a focus on time series data, facilitating fair and comprehensive evaluations. By integrating the Co-12 properties of Nauta et al., this framework allows for effectively comparing prototype methods against each other and against other XAI methods, ultimately assisting practitioners in selecting appropriate explanation methods while minimizing the costs associated with user studies. All code is publicly available at https://github.com/HelenaM23/ProtoScore .

This paper examines the ethical and anthropological challenges posed by AI-driven recommender systems (RSs), which increasingly shape digital environments and social interactions. By curating personalized content, RSs do not merely reflect user preferences but actively construct experiences across social media, entertainment platforms, and e-commerce. Their influence raises concerns over privacy, autonomy, and mental well-being, while existing approaches such as "algorethics" - the effort to embed ethical principles into algorithmic design - remain insufficient. RSs inherently reduce human complexity to quantifiable profiles, exploit user vulnerabilities, and prioritize engagement over well-being. The paper advances a three-dimensional framework for human-centered RSs, integrating policies and regulation, interdisciplinary research, and education. These strategies are mutually reinforcing: research provides evidence for policy, policy enables safeguards and standards, and education equips users to engage critically. By connecting ethical reflection with governance and digital literacy, the paper argues that RSs can be reoriented to enhance autonomy and dignity rather than undermine them.

As algorithms increasingly mediate competitive decision-making, their influence extends beyond individual outcomes to shaping strategic market dynamics. In two preregistered experiments, we examined how algorithmic advice affects human behavior in classic economic games with unique, non-collusive, and analytically traceable equilibria. In Experiment 1 (N = 107), participants played a Bertrand price competition with individualized or collective algorithmic recommendations. Initially, collusively upward-biased advice increased prices, particularly when individualized, but prices gradually converged toward equilibrium over the course of the experiment. However, participants avoided setting prices above the algorithm's recommendation throughout the experiment, suggesting that advice served as a soft upper bound for acceptable prices. In Experiment 2 (N = 129), participants played a Cournot quantity competition with equilibrium-aligned or strategically biased algorithmic recommendations. Here, individualized equilibrium advice supported stable convergence, whereas collusively downward-biased advice led to sustained underproduction and supracompetitive profits - hallmarks of tacit collusion. In both experiments, participants responded more strongly and consistently to individualized advice than collective advice, potentially due to greater perceived ownership of the former. These findings demonstrate that algorithmic advice can function as a strategic signal, shaping coordination even without explicit communication. The results echo real-world concerns about algorithmic collusion and underscore the need for careful design and oversight of algorithmic decision-support systems in competitive environments.