This landscape scan was conducted by Invest in Open Infrastructure in the context of the Building Resilient Infrastructure through Dialogue, Growth, and Exchange project exploring how stakeholders in the knowledge ecosystem can work together as AI reshapes how collections are used and valued. Curated collections form part of the digital commons: shared open resources maintained academic institutions, non-profit organizations, governments, and private companies. Sustained largely by public and private funding and an ethos of open knowledge sharing, these collections represent a public good inseparable from the health of open science and democratic access to information. That infrastructure is now under strain. AI labs bring insatiable demand for immediate access to high-quality training data. Automated bots now generate traffic that, in some cases, exceeds human visits, overwhelming servers andinflating costs. AI-generated submissions threatens to overwhelm editorial workflows. These bots include large technology companies and a significant long tail of start-ups and individual experimenters. In response, collections stewards have deployed bot-blocking tools, updated access policies, and pursued licensing strategies. Yet these responses have proven inadequate. Technical countermeasures are routinely circumvented. Licensing frameworks are nascent and poorly enforced. Legal protections are fragmented and oriented toward intellectual property rather than broader harms to the commons. Defensive access restrictions risk accelerating data consolidation, undermining the openness they were designed to protect. The report points toward a more promising path: commons-based governance grounded in reciprocal norms and shared interests. Data users have concrete reasons to want the commons to survive: loss of key data sources reduces training data quality; an increasingly walled-off web raises legal risks; and public frustration creates reputational pressure. Well-maintained curated collections offer unique, authoritative content that produces better models. Investment in the commons, properly framed, is investment in the quality of AI. Yet a central tension remains unresolved. Existing frameworks rely on voluntary compliance, while evidence shows that voluntary frameworks have so far failed to change behaviour at scale. Whether enlightened self-interest will prove more effective than thelegal and technical mechanisms that have already fallen short remains an open question. Answering it requires engaging curators and consumers of open collections as stewards of the digital commons, co-creating partnership models that align open knowledge strategies with commercial demand.
Learn how to use AI effectively in research whilst maintaining responsible human oversight, with a library of guidelines, tools and copy and paste prompts.
Frontiers have released, High-impact AI: A researcher's playbook, a resource covering the entire publication lifecycle – from researchers to editors and peer reviewers. This framework provides clear, operational routes forward for AI use in every publishing role (whether researcher, editor, or reviewer), promoting AI use that is accountable, transparent, risk-aware, and innovation-enabling. Frontiers retains the principle that the human remains accountable and translates it into responsible practice through the BE WISE framework: B — Be transparent E — Ensure accountability W — Work with the right tools I — Inform yourself S — Safeguard integrity E — Embed equity
This report presents findings from the Relational Futures project, an Indigenous led study examining how Aboriginal and Torres Strait Islander peoples are encountering and responding to artificial intelligence (AI), including generative systems, automated decision-making tools, and AI companions. It demonstrates that artificial intelligence is not only a technical issue, but a relational, cultural and political one.
Applies to England About this advice note This advice note supports awarding organisations (AOs) in understanding how the existing Conditions of Recognition[footnote 1] and related Guidance apply to the risks of malpractice arising from Learners’ use of artificial intelligence (AI) tools.
The public are understandably worried about AI and, so far, governments have struggled to articulate a clear vision for what it would mean for AI to go wel
Chaired by Ed Fay, Director of Library Services and University Librarian at the University of Bristol.
Lynda Kellam, Ph.D., is the Snyder-Granader Director of Research Data and Digital Scholarship at the University of Pennsylvania Libraries and a co-founder of the Data Rescue Project. Her work focuses on the U.S. government's data infrastructure and enhancing access, resilience, and community support for the nation’s public data resources. She is driven by the belief that public data is a civic asset, essential to research, democratic accountability, and an informed public life.
With contributions from more than 120 experts across 28 countries and regions, including 26 heads of state and ministerial-level officials, this report examines how artificial intelligence (AI) is increasingly embedded across energy systems, infrastructure, manufacturing, logistics, finance, certification, education and public governance. The report analyzes how AI is transforming climate mitigation and adaptation in practice, and what is required to translate technical capability into scalable sustainability outcomes. Rather than treating AI as a standalone technological breakthrough, the report emphasizes its role in reducing uncertainty, improving decision quality, and aligning complex systems under real-world constraints.
AI can play a decisive role in making digital government records more accessible and manageable, provided that its use is grounded in responsibility and clear purpose. Work is already underway across archives and government, where AI is being used to manage scale, improve accuracy, and enhance public access to digital records – including email, PDFs, spreadsheets, images, scanned documents, audiovisual assets, and social media posts. Building on these foundations, the GLOW study identifies four interlinked priorities for responsible and effective adoption. First, AI should be applied where it can deliver clear, measurable benefits – particularly in appraisal and selection, sensitivity review, and metadata enrichment for both textual and audiovisual materials. Interviewees repeatedly stressed that the sheer volume of born-digital records now exceeds human capacity, making manual processing unrealistic. As John Sheridan (The National Archives UK) described, effective archival AI workflows will resemble a series of sieves: simple tools handling early filtering (e.g., filetype detection, basic entity extraction), followed by increasingly sophisticated machine learning and language model techniques that surface higher value material for expert review. Used in this layered way, automation can strengthen efficiency and consistency without displacing professional judgement. For example, AI can flag personal or confidential information, identify clusters of potentially significant correspondence, or reveal hidden risks within complex files – reducing the time specialists spend on mechanical triage and enabling them to focus on interpretive and high-stakes decision making. Second, ChatGPT and other generative AI tools have modified users’ expectations. Users increasingly expect instant, conversational access to archival information; AI‑generated summaries rather than raw documents; cross‑collection synthesis; intelligent handling of poor metadata; and personalised research support – expectations that exceed the capabilities of many archival systems. To adapt to these changing expectations, archival institutions are experimenting with new techniques and protocols such as MCP (Model Context Protocol) and RAG (Retrieval-Augmented Generation). Used together, MCP and RAG align GenAI discovery with archival values of provenance, authenticity, accountability, and user trust. Third, progress towards responsible AI depends on implementing a clear and accountable framework. Automation must operate within systems that guarantee transparency, traceability, and security, supported by training and governance that ensure ethical use. Many institutions are already developing policies and guidance, but a unified framework would help align practice across departments and institutions, and safeguard the integrity of public records. A clear and accountable framework for responsible AI should answer questions of purpose, transparency, human oversight, risk/security, and accountability/auditability. Fourth, the study calls for a coordinated national strategy that connects these efforts. The National Archives (UK), working with ministerial departments and other administrations, is well placed to lead this work in partnership with the wider GLAM and academic sectors. International collaboration with bodies such as the National Archives and Records Administration (NARA) in the United States and the European Archives Group could extend these principles globally. Through this joined-up approach, which would integrate technology, policy, and human expertise, AI can strengthen public trust and ensure that the digital record remains secure and accessible.
The rising cost of living and new technology are leading to record levels of exploitation in the UK, the independent anti-slavery commissioner (IASC) has warned.
By mid-2025, roughly 35% of newly published websites were classified as AI-generated or AI-assisted. We analyze the impact on semantic diversity, factual accuracy, sentiment, and more.
Generative AI agents are pitched as being a new gateway to engaging with the Internet, but the way AI sees the Internet is both "conservative" and "stubborn”.
Launch of ‘Civil Society Joint Action for Strengthening Accountability and Public Interest in AI’ Aiming to Establish Accountability, Public Interest, and Democratic Governance in AI Participation of 41 civil society organizations nationwide covering human rights, labor, welfare, gender, environme
AI-powered scientific research tools are rapidly being integrated into research workflows, yet the field lacks a clear lens into how researchers use these systems in real-world settings. We present and analyze the Asta Interaction Dataset, a large-scale resource comprising over 200,000 user queries and interaction logs from two deployed tools (a literature discovery interface and a scientific question-answering interface) within an LLM-powered retrieval-augmented generation platform. Using this dataset, we characterize query patterns, engagement behaviors, and how usage evolves with experience. We find that users submit longer and more complex queries than in traditional search, and treat the system as a collaborative research partner, delegating tasks such as drafting content and identifying research gaps. Users treat generated responses as persistent artifacts, revisiting and navigating among outputs and cited evidence in non-linear ways. With experience, users issue more targeted queries and engage more deeply with supporting citations, although keyword-style queries persist even among experienced users. We release the anonymized dataset and analysis with a new query intent taxonomy to inform future designs of real-world AI research assistants and to support realistic evaluation.
The study examines the surge in GenAI chatbot mentions in scientific literature, showing a 13-fold increase from November 2022 to December 2023. The use of GenAI chatbots in scientific research is mainly in ICT and Applied Sciences, where AI improves research efficiency. Key applications include writing and practical implementation, demonstrating the tool's widespread use in academic writing and research. Nonetheless, the increasing use of AI in research and academia raises concerns about quality assurance and trust issue.
UNESCO has launched the latest edition of its flagship report Re|Shaping Policies for Creativity, which analyses a rapidly evolving cultural landscape shaped by digital transformation, Artificial Intelligence (AI), shifting global trade dynamics, and mounting threats to artistic freedom. A global monitoring report with data from over 120 countries, it points out the need for stronger policies to protect creators from widening inequalities.
Some 94 per cent of students say they use generative AI to help with assessed work, according to this year’s HEPI/Kortext student generative AI survey. The findings, based on a Savanta survey of just over 1,000 full-time undergraduates, are said to show a “somewhat polarised AI landscape,” with a roughly even split between students who say AI makes them feel more or less lonely, between those students who felt their institution encourages AI use and those who did not, and between those students who preferred using AI and those who favoured traditional research sources.
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