AI risks entrenching racism and sexism in Australia, the human rights commissioner has warned, amid internal Labor debate about how to respond to the emerging technology. Lorraine Finlay says the pursuit of productivity gains from AI should not come at the expense of discrimination if the technology is not properly regulated. Finlay's comments follow Labor senator Michelle Ananda-Rajah breaking ranks to call for all Australian data to be "freed" to tech companies to prevent AI perpetuating overseas biases and reflect Australian life and culture. Ananda-Rajah is opposed to a dedicated AI act but believes content creators should be paid for their work. Media and arts groups have warned of "rampant theft" of intellectual property if big tech companies can take their content to train AI models.

Google's dominance of the search engine and online advertising market has come under intense scrutiny, with the technology giant embroiled in years of legal wrangling as part of two antitrust cases. A US federal judge is expected to issue a ruling this month that could see Google being ordered to break up its search business. The company has said it would appeal such a ruling, saying the idea of spinning off Chrome was an "unprecedented proposal" that would harm consumers and security. A spokesman for Perplexity told the BBC that its bid marks an "important commitment to the open web, user choice, and continuity for everyone who has chosen Chrome." As part of the proposed takeover, Perplexity said it would continue to have Google as the default search engine within Chrome, though users could adjust their settings.

Big Brother Watch is bringing a legal challenge against the Met Police's use of the technology, alongside Shaun Thompson, who was wrongly identified by an LFR camera. Rebecca Vincent, interim director of Big Brother Watch, said: "Police have interpreted the absence of any legislative basis authorising the use of this intrusive technology as carte blanche to continue to roll it out unfettered, despite the fact that a crucial judicial review on the matter is pending. "The Home Office must scrap its plans to roll out further live facial recognition capacity until robust legislative safeguards are established." Charlie Whelton, policy and campaigns officer at Liberty, said: "It's welcome news that the government will finally develop a statutory framework on the use of facial recognition, but this should be in place before more facial recognition technology is rolled out. "There's no reasonable excuse to be putting even more cameras on our streets before the public have had their say and legislation is brought in to protect all of us." The government says officers using the LFR vans will need to follow the College of Policing's guidance on the technology and the Surveillance Camera Code of Practice.

The rapid emergence of large language models (LLMs) has raised urgent questions across the modern workforce about this new technology's strengths, weaknesses, and capabilities. For privacy professionals, the question is whether these AI systems can provide reliable support on regulatory compliance, privacy program management, and AI governance. In this study, we evaluate ten leading open and closed LLMs, including models from OpenAI, Anthropic, Google DeepMind, Meta, and DeepSeek, by benchmarking their performance on industry-standard certification exams: CIPP/US, CIPM, CIPT, and AIGP from the International Association of Privacy Professionals (IAPP). Each model was tested using official sample exams in a closed-book setting and compared to IAPP's passing thresholds. Our findings show that several frontier models such as Gemini 2.5 Pro and OpenAI's GPT-5 consistently achieve scores exceeding the standards for professional human certification - demonstrating substantial expertise in privacy law, technical controls, and AI governance. The results highlight both the strengths and domain-specific gaps of current LLMs and offer practical insights for privacy officers, compliance leads, and technologists assessing the readiness of AI tools for high-stakes data governance roles. This paper provides an overview for professionals navigating the intersection of AI advancement and regulatory risk and establishes a machine benchmark based on human-centric evaluations.

Natural disasters such as hurricanes and wildfires increasingly introduce unusual disturbance on economic activities, which are especially likely to reshape commercial land use pattern given their sensitive to customer visitation. However, current modeling approaches are limited in capturing such complex interplay between human activities and commercial land use change under and following disturbances. Such interactions have been more effectively captured in current resilient urban planning theories. This study designs and calibrates a Urban Theory-Informed Spatio-Temporal Attention Model for Predicting Post-Disaster Commercial Land Use Change (Urban-STA4CLC) to predict both the yearly decline and expansion of commercial land use at census block level under cumulative impact of disasters on human activities over two years. Guided by urban theories, Urban-STA4CLC integrates both spatial and temporal attention mechanisms with three theory-informed modules. Resilience theory guides a disaster-aware temporal attention module that captures visitation dynamics. Spatial economic theory informs a multi-relational spatial attention module for inter-block representation. Diffusion theory contributes a regularization term that constrains land use transitions. The model performs significantly better than non-theoretical baselines in predicting commercial land use change under the scenario of recurrent hurricanes, with around 19% improvement in F1 score (0.8763). The effectiveness of the theory-guided modules was further validated through ablation studies. The research demonstrates that embedding urban theory into commercial land use modeling models may substantially enhance the capacity to capture its gains and losses. These advances in commercial land use modeling contribute to land use research that accounts for cumulative impacts of recurrent disasters and shifts in economic activity patterns.

Operationalizing the EU AI Act requires clear technical documentation to ensure AI systems are transparent, traceable, and accountable. Existing documentation templates for AI systems do not fully cover the entire AI lifecycle while meeting the technical documentation requirements of the AI Act. This paper addresses those shortcomings by introducing open-source templates and examples for documenting data, models, and applications to provide sufficient documentation for certifying compliance with the AI Act. These templates track the system's status over the entire AI lifecycle, ensuring traceability, reproducibility, and compliance with the AI Act. They also promote discoverability and collaboration, reduce risks, and align with best practices in AI documentation and governance. The templates are evaluated and refined based on user feedback to enable insights into their usability and implementabil-ity. We then validate the approach on real-world scenarios, providing examples that further guide their implementation: the data template is followed to document a skin tones dataset created to support fairness evaluations of downstream computer vision models and human-centric applications; the model template is followed to document a neural network for segmenting human silhouettes in photos. The application template is tested on a system deployed for construction site safety using real-time video analytics and sensor data. Our results show that TechOps can serve as a practical tool to enable oversight for regulatory compliance and responsible AI development.

Large language models (LLMs) acquire vast knowledge from large text corpora, but this information can become outdated or inaccurate. Since retraining is computationally expensive, knowledge editing offers an efficient alternative -- modifying internal knowledge without full retraining. These methods aim to update facts precisely while preserving the model's overall capabilities. While existing surveys focus on the mechanism of editing (e.g., parameter changes vs. external memory), they often overlook the function of the knowledge being edited. This survey introduces a novel, complementary function-based taxonomy to provide a more holistic view. We examine how different mechanisms apply to various knowledge types -- factual, temporal, conceptual, commonsense, and social -- highlighting how editing effectiveness depends on the nature of the target knowledge. By organizing our review along these two axes, we map the current landscape, outline the strengths and limitations of existing methods, define the problem formally, survey evaluation tasks and datasets, and conclude with open challenges and future directions.

Video dubbing aims to translate original speech in visual media programs from the source language to the target language, relying on neural machine translation and text-to-speech technologies. Due to varying information densities across languages, target speech often mismatches the source speech duration, causing audio-video synchronization issues that significantly impact viewer experience. In this study, we approach duration alignment in LLM-based video dubbing machine translation as a preference optimization problem. We propose the Segment Supervised Preference Optimization (SSPO) method, which employs a segment-wise sampling strategy and fine-grained loss to mitigate duration mismatches between source and target lines. Experimental results demonstrate that SSPO achieves superior performance in duration alignment tasks.

As AI becomes more "agentic," it faces technical and socio-legal issues it must address if it is to fulfill its promise of increased economic productivity and efficiency. This paper uses technical and legal perspectives to explain how things change when AI systems start being able to directly execute tasks on behalf of a user. We show how technical conceptions of agents track some, but not all, socio-legal conceptions of agency. That is, both computer science and the law recognize the problems of under-specification for an agent, and both disciplines have robust conceptions of how to address ensuring an agent does what the programmer, or in the law, the principal desires and no more. However, to date, computer science has under-theorized issues related to questions of loyalty and to third parties that interact with an agent, both of which are central parts of the law of agency. First, we examine the correlations between implied authority in agency law and the principle of value-alignment in AI, wherein AI systems must operate under imperfect objective specification. Second, we reveal gaps in the current computer science view of agents pertaining to the legal concepts of disclosure and loyalty, and how failure to account for them can result in unintended effects in AI ecommerce agents. In surfacing these gaps, we show a path forward for responsible AI agent development and deployment.

--Access to real-world medical data is often restricted due to privacy regulations, posing a significant barrier to the advancement of healthcare research. Synthetic data offers a promising alternative; however, generating realistic, clinically valid, and privacy-conscious records remains a major challenge. Recent advancements in Large Language Models (LLMs) offer new opportunities for structured data generation; however, existing approaches frequently lack systematic prompting strategies and comprehensive, multi-dimensional evaluation frameworks. In this paper, we present SynLLM, a modular framework for generating high-quality synthetic medical tabular data using 20 state-of-the-art open-source LLMs, including LLaMA, Mistral, and GPT variants, guided by structured prompts. We propose four distinct prompt types, ranging from example-driven to rule-based constraints, that encode schema, metadata, and domain knowledge to control generation without model fine-tuning. Our framework features a comprehensive evaluation pipeline that rigorously assesses generated data across statistical fidelity, clinical consistency, and privacy preservation. We evaluate SynLLM across three public medical datasets, including Diabetes, Cirrhosis, and Stroke, using 20 open-source LLMs. Our results show that prompt engineering significantly impacts data quality and privacy risk, with rule-based prompts achieving the best privacy-quality balance. SynLLM establishes that, when guided by well-designed prompts and evaluated with robust, multi-metric criteria, LLMs can generate synthetic medical data that is both clinically plausible and privacy-aware, paving the way for safer and more effective data sharing in healthcare research. Access to real-world medical data is frequently restricted due to privacy regulations, ethical constraints, and institutional barriers, posing a significant challenge for the development of AI-driven healthcare solutions. While data protection laws such as the Health Insurance Portability and Accountability Act (HIP AA) [11] and the General Data Protection Regulation (GDPR) [37] are essential for safeguarding patient confidentiality, they often hinder the availability of data for clinical model development and research.