Online behavioural research faces an emerging threat as participants increasingly turn to large language models (LLMs) for advice, translation, or task delegation: LLM Pollution. We identify three interacting variants through which LLM Pollution threatens the validity and integrity of online behavioural research. First, Partial LLM Mediation occurs when participants make selective use of LLMs for specific aspects of a task, such as translation or wording support, leading researchers to (mis)interpret LLM-shaped outputs as human ones. Second, Full LLM Delegation arises when agentic LLMs complete studies with little to no human oversight, undermining the central premise of human-subject research at a more foundational level. Third, LLM Spillover signifies human participants altering their behaviour as they begin to anticipate LLM presence in online studies, even when none are involved. While Partial Mediation and Full Delegation form a continuum of increasing automation, LLM Spillover reflects second-order reactivity effects. Together, these variants interact and generate cascading distortions that compromise sample authenticity, introduce biases that are difficult to detect post hoc, and ultimately undermine the epistemic grounding of online research on human cognition and behaviour. Crucially, the threat of LLM Pollution is already co-evolving with advances in generative AI, creating an escalating methodological arms race. To address this, we propose a multi-layered response spanning researcher practices, platform accountability, and community efforts. As the challenge evolves, coordinated adaptation will be essential to safeguard methodological integrity and preserve the validity of online behavioural research.

Scientific paper retrieval, particularly framed as document-to-document retrieval, aims to identify relevant papers in response to a long-form query paper, rather than a short query string. Previous approaches to this task have focused exclusively on abstracts, embedding them into dense vectors as surrogates for full documents and calculating similarity between them. Yet, abstracts offer only sparse and high-level summaries, and such methods primarily optimize one-to-one similarity, overlooking the dynamic relations that emerge among relevant papers during the retrieval process. To address this, we propose Chain of Retrieval(COR), a novel iterative framework for full-paper retrieval. Specifically, CoR decomposes each query paper into multiple aspect-specific views, matches them against segmented candidate papers, and iteratively expands the search by promoting top-ranked results as new queries, thereby forming a tree-structured retrieval process. The resulting retrieval tree is then aggregated in a post-order manner: descendants are first combined at the query level, then recursively merged with their parent nodes, to capture hierarchical relations across iterations. To validate this, we present SCIFULLBENCH, a large-scale benchmark providing both complete and segmented contexts of full papers for queries and candidates, and results show that CoR significantly outperforms existing retrieval baselines. Our code and dataset is available at https://github.com/psw0021/Chain-of-Retrieval.git.

Oversight and control, which we collectively call supervision, are often discussed as ways to ensure that AI systems are accountable, reliable, and able to fulfill governance and management requirements. However, the requirements for "human oversight" risk codifying vague or inconsistent interpretations of key concepts like oversight and control. This ambiguous terminology could undermine efforts to design or evaluate systems that must operate under meaningful human supervision. This matters because the term is used by regulatory texts such as the EU AI Act. This paper undertakes a targeted critical review of literature on supervision outside of AI, along with a brief summary of past work on the topic related to AI. We next differentiate control as ex-ante or real-time and operational rather than policy or governance, and oversight as performed ex-post, or a policy and governance function. Control aims to prevent failures, while oversight focuses on detection, remediation, or incentives for future prevention. Building on this, we make three contributions. 1) We propose a framework to align regulatory expectations with what is technically and organizationally plausible, articulating the conditions under which each mechanism is possible, where they fall short, and what is required to make them meaningful in practice. 2) We outline how supervision methods should be documented and integrated into risk management, and drawing on the Microsoft Responsible AI Maturity Model, we outline a maturity model for AI supervision. 3) We explicitly highlight boundaries of these mechanisms, including where they apply, where they fail, and where it is clear that no existing methods suffice. This foregrounds the question of whether meaningful supervision is possible in a given deployment context, and can support regulators, auditors, and practitioners in identifying both present and future limitations.

Foundation Models (FMs) are large-scale, pre-trained artificial intelligence (AI) systems that have revolutionized natural language processing and computer vision, and are now advancing geospatial analysis and Earth Observation (EO). They promise improved generalization across tasks, scalability, and efficient adaptation with minimal labeled data. However, despite the rapid proliferation of geospatial FMs, their real-world utility and alignment with global sustainability goals remain underexplored. We introduce SustainFM, a comprehensive benchmarking framework grounded in the 17 Sustainable Development Goals with extremely diverse tasks ranging from asset wealth prediction to environmental hazard detection. This study provides a rigorous, interdisciplinary assessment of geospatial FMs and offers critical insights into their role in attaining sustainability goals. Our findings show: (1) While not universally superior, FMs often outperform traditional approaches across diverse tasks and datasets. (2) Evaluating FMs should go beyond accuracy to include transferability, generalization, and energy efficiency as key criteria for their responsible use. (3) FMs enable scalable, SDG-grounded solutions, offering broad utility for tackling complex sustainability challenges. Critically, we advocate for a paradigm shift from model-centric development to impact-driven deployment, and emphasize metrics such as energy efficiency, robustness to domain shifts, and ethical considerations.

Privacy policies are statements that notify users of the services' data practices. However, few users are willing to read through policy texts due to the length and complexity. While automated tools based on machine learning exist for privacy policy analysis, to achieve high classification accuracy, classifiers need to be trained on a large labeled dataset. Most existing policy corpora are labeled by skilled human annotators, requiring significant amount of labor hours and effort. In this paper, we leverage active learning and crowdsourcing techniques to develop an automated classification tool named Calpric (Crowdsourcing Active Learning PRIvacy Policy Classifier), which is able to perform annotation equivalent to those done by skilled human annotators with high accuracy while minimizing the labeling cost. Specifically, active learning allows classifiers to proactively select the most informative segments to be labeled. On average, our model is able to achieve the same F1 score using only 62% of the original labeling effort. Calpric's use of active learning also addresses naturally occurring class imbalance in unlabeled privacy policy datasets as there are many more statements stating the collection of private information than stating the absence of collection. By selecting samples from the minority class for labeling, Calpric automatically creates a more balanced training set.

Trump threatens to walk out on Norah O'Donnell as 60 Minutes EDITS OUT astonishing meltdown White House makes'venomous' split with Israel: Fiery feud engulfs Trump insiders with alliance on the brink I won't ever forget what I saw at Andy Cohen's party. He may admit he's hooking up with guys on every dating app but this is the truth about men like him: KENNEDY Sad secrets of privileged son, 20, accused of murdering his self-made single mother near their $1.9m home, then screaming'Mama' Three Americans among seven killed when avalanche obliterates Himalayan climbers' base camp Thomas Massie remarries 16 months after losing wife of 31 years... as Trump ally launches sick attack Trump stuns 60 Minutes' Norah O'Donnell as he breaks terrifying news about China and Russia nukes Ex-CIA spy shares an easy way to tell if someone is lying... and the tactic he uses to strengthen his love life Justin Baldoni's bombshell $400M case against Blake Lively and Ryan Reynolds is'formally ended by a judge' JD Vance declares himself'UFO' lunatic as he vows to pull back the curtain on government secrets Sex aids and poppers... the sordid discoveries made by royal aides after party Andrew threw for Epstein and Ghislaine Maxwell - and the truth about those massages: ROBERT JOBSON Top Democrat lawmaker becomes international fugitive after she was freed on bail'for stealing thousands from vulnerable man, 83' George Clooney gives rare insight into life with wife Amal and their twins - as he details his relationship with his kids, lauds his'beautiful' family and brands himself'very lucky' Shohei Ohtani's wife makes rare appearance to celebrate Dodgers star's World Series win I learned the horrifying risks of'miracle' ADHD drugs and stopped taking them... but it was too late A girl, 15, bludgeoned to death in a gated enclave, a Kennedy cousin released and the brother who'knows the truth' about the death that haunts Camelot Justin Trudeau's rapper son sounds worse than ever in latest music video despite father's burgeoning romance with Katy Perry Moment'knifeman who hurt 11 people in Huntingdon train rampage storms barber shop moments after stabbing 14-year-old boy' Meghan is mocked for her new Christmas recipe... boiled water! Chefs, your jobs are safe for now! Robots might be poised to replace humans in factories and warehouses, but chefs don't need to worry about losing their jobs anytime soon. In a viral video, which has amassed over 6.3 million views, a humanoid robot attempts to make a stir-fry for its owner - with disastrous results.

Arcadia Publishing built its empire on small-town storytellers. Now it wants to sell their words to an A.I. company no one will name. Enter your email to receive alerts for this author. You can manage your newsletter subscriptions at any time. You're already subscribed to the aa_Nitish_Pahwa newsletter. You can manage your newsletter subscriptions at any time.

U.S. District Court Judge Karin Immergut extends injunction blocking Trump administration from deploying National Guard troops to Portland until Friday.

China waged a campaign of harassment and intimidation directed at a UK university to get it to shut down sensitive research into alleged human rights abuses, documents seen by the BBC show. Sheffield Hallam University staff in China were threatened by individuals described by them as being from China's National Security Service who demanded the research being done in Sheffield be halted. And access to the university's websites from China was blocked, impeding its ability to recruit Chinese students, in a campaign of threats and intimidation lasting more than two years. In an internal email from July 2024, university officials said attempting to retain the business in China and publication of the research are now untenable bedfellows. When the UK government learned of the case, the then Foreign Secretary David Lammy issued a warning to his Chinese counterpart that it would not tolerate attempts to suppress academic freedoms at UK universities, the BBC understands.

I built this'AI aunt' for women after family tragedy in South Africa A gruesome killing in her own family inspired South African Leonora Tima to create a digital platform where people, mostly women, can talk about and track abuse. Leonora's relative was just 19 years old, and nine months pregnant, when she was killed, her body dumped on the side of a highway near Cape Town in 2020. I work in the development sector, so I've seen violence, Leonora says. But what stood out for me was that my family member's violent death was seen as so normal in South African society. Her death wasn't published by any news outlet because the sheer volume of these cases in our country is such that it doesn't qualify as news.