Chi Onwurah, the committee chair, has said she was disappointed in the government's response on AI and digital advertising in particular. Chi Onwurah, the committee chair, has said she was disappointed in the government's response on AI and digital advertising in particular. Failures to properly tackle online misinformation mean it is "only a matter of time" before viral content triggers a repeat of the 2024 summer riots, MPs have warned. Chi Onwurah, the chair of the Commons science and technology select committee, said ministers seemed complacent about the threat and this was putting the public at risk. The committee said it was disappointed in the government's response to its recent report warning social media companies' business models contributed to disturbances after the Southport murders .

The Stream Why does misinformation spread faster than truth? We examine how online misinformation spreads and the efforts to stop it. From bots pushing ready-made talking points, to AI generating information that isn't always reliable, to pages distorting images and bending facts - online misinformation is everywhere. So how can people tell what's real? And is it even possible to build immunity against misinformation in today's digital world?

Anti-vaccine activist Robert F. Kennedy Jr.'s presidential campaign hosted an online panel Wednesday on the future of AI moderated, for some reason, by Ian Carroll, a self-styled journalist with a history of antisemitic statements. In the course of the conversation, Kennedy admitted that he "gets manipulated by AI all the time." "Somebody will send me something and I'll go'Holy cow, did you see this?'," he said, describing how he credulously forwards fake content to his children, only for them to have to correct him. RFK Jr. said he regularly "gets manipulated by AI." While Carroll has no particular public profile on AI, his persona tracks with the campaign's focus on tech figures and influencers as it courts a young, male, and extremely online audience.

AI expert Marva Bailer tells Fox News Digital how the open availability of artificial intelligence can have negative impacts and talks potential federal legislation to control it. Books about the dizzying impact of the internet and artificial intelligence are among finalists for a new book prize that aims to help fix the gender imbalance in nonfiction publishing. The shortlisted six books for the inaugural Women's Prize for Nonfiction, announced on Wednesday, include Canadian author-activist Naomi Klein's "Doppleganger," a plunge into online misinformation, and British journalist Madhumita Murgia's "Code-Dependent: Living in the Shadow of AI." The 38,000 award is a sister to the 29-year-old Women's Prize for Fiction and is open to female English-language writers from any country in any nonfiction genre. The finalists also include autobiographical works -- poet Safiya Sinclair's "How to Say Babylon: A Jamaican Memoir" and British art critic Laura Cumming's "Thunderclap: A Memoir of Art and Life and Sudden Death."

The proliferation of online misinformation has posed significant threats to public interest. While numerous online users actively participate in the combat against misinformation, many of such responses can be characterized by the lack of politeness and supporting facts. As a solution, text generation approaches are proposed to automatically produce counter-misinformation responses. Nevertheless, existing methods are often trained end-to-end without leveraging external knowledge, resulting in subpar text quality and excessively repetitive responses. In this paper, we propose retrieval augmented response generation for online misinformation (RARG), which collects supporting evidence from scientific sources and generates counter-misinformation responses based on the evidences. In particular, our RARG consists of two stages: (1) evidence collection, where we design a retrieval pipeline to retrieve and rerank evidence documents using a database comprising over 1M academic articles; (2) response generation, in which we align large language models (LLMs) to generate evidence-based responses via reinforcement learning from human feedback (RLHF). We propose a reward function to maximize the utilization of the retrieved evidence while maintaining the quality of the generated text, which yields polite and factual responses that clearly refutes misinformation. To demonstrate the effectiveness of our method, we study the case of COVID-19 and perform extensive experiments with both in- and cross-domain datasets, where RARG consistently outperforms baselines by generating high-quality counter-misinformation responses.

The proliferation of online misinformation has emerged as one of the biggest threats to society. Considerable efforts have focused on building misinformation detection models, still the perils of misinformation remain abound. Mitigating online misinformation and its ramifications requires a holistic approach that encompasses not only an understanding of its intricate landscape in relation to the complex issue and topic-rich information ecosystem online, but also the psychological drivers of individuals behind it. Adopting a time series analytic technique and robust causal inference-based design, we conduct a large-scale observational study analyzing over 32 million COVID-19 tweets and 16 million historical timeline tweets. We focus on understanding the behavior and psychology of users disseminating misinformation during COVID-19 and its relationship with the historical inclinations towards sharing misinformation on Non-COVID domains before the pandemic. Our analysis underscores the intricacies inherent to cross-domain misinformation, and highlights that users' historical inclination toward sharing misinformation is positively associated with their present behavior pertaining to misinformation sharing on emergent topics and beyond. This work may serve as a valuable foundation for designing user-centric inoculation strategies and ecologically-grounded agile interventions for effectively tackling online misinformation.

With the rapid growth and spread of online misinformation, people need tools to help them evaluate the credibility and accuracy of online information. Lateral reading, a strategy that involves cross-referencing information with multiple sources, may be an effective approach to achieving this goal. In this paper, we present ReadProbe, a tool to support lateral reading, powered by generative large language models from OpenAI and the Bing search engine. Our tool is able to generate useful questions for lateral reading, scour the web for relevant documents, and generate well-attributed answers to help people better evaluate online information. We made a web-based application to demonstrate how ReadProbe can help reduce the risk of being misled by false information. The code is available at https://github.com/DakeZhang1998/ReadProbe. An earlier version of our tool won the first prize in a national AI misinformation hackathon.

It's been quite an interesting journey for Factmata since we started in January and we're now about to launch a tool that puts factual context in the hands of the people. This will happen around the UK general election, and marks the completion of our Google Digital News Initiative (DNI) project. For 5 months, we've been working around the clock with a distributed team of NLP researchers, PhDs and scientists from around the world to build this, and now finishing off the final touches. As we prepare for launch, we wanted to tell the world about what's next and where we want to take Factmata in the future. Given our team's work in automated fact-checking in previous research, we are uniquely placed to build AI to solve the problem of online misinformation.