Wealthy tech entrepreneurs including Elon Musk launched OpenAI in 2015 as a nonprofit research lab that they said would involve society and the public in the development of powerful AI, unlike Google and other giant tech companies working behind closed doors. In line with that spirit, OpenAI's reports to US tax authorities have from its founding said that any member of the public can review copies of its governing documents, financial statements, and conflict of interest rules. But when WIRED requested those records last month, OpenAI said its policy had changed, and the company provided only a narrow financial statement that omitted the majority of its operations. "We provide financial statements when requested," company spokesperson Niko Felix says. "OpenAI aligns our practices with industry standards, and since 2022 that includes not publicly distributing additional internal documents."

Artificial intelligence will make it difficult to spot whether emails are genuine or sent by scammers and malicious actors, including messages that ask computer users to reset their passwords, the UK's cybersecurity agency has warned. The National Cyber Security Centre (NCSC) said people would struggle to identify phishing messages – where users are tricked into handing over passwords or personal details – due to the sophistication of AI tools. Generative AI, the term for technology that can produce convincing text, voice and images from simple hand-typed prompts, has become widely available to the public through chatbots such as ChatGPT and free-to-use versions known as open source models. The NCSC, part of the GCHQ spy agency, said in its latest assessment of AI's impact on the cyber threats facing the UK that AI would "almost certainly" increase the volume of cyber-attacks and heighten their impact over the next two years. It said generative AI and large language models – the technology that underpins chatbots – will complicate efforts to identify different types of attack such as spoof messages and social engineering, the term for manipulating people to hand over confidential material.

They present new challenges to contextual confidence, disrupting participants' ability to identify the authentic context of communication and their ability to protect communication from reuse and recombination outside its intended context. In this paper, we describe strategies - tools, technologies and policies - that aim to stabilize communication in the face of these challenges. The strategies we discuss fall into two broad categories. Containment strategies aim to reassert context in environments where it is currently threatened - a reaction to the context-free expectations and norms established by the internet. Mobilization strategies, by contrast, view the rise of generative AI as an opportunity to proactively set new and higher expectations around privacy and authenticity in mediated communication.

Image generation using generative AI is rapidly becoming a major new source of visual media, with billions of AI generated images created using diffusion models such as Stable Diffusion and Midjourney over the last few years. In this paper we collect and analyse over 3 million prompts and the images they generate. Using natural language processing, topic analysis and visualisation methods we aim to understand collectively how people are using text prompts, the impact of these systems on artists, and more broadly on the visual cultures they promote. Our study shows that prompting focuses largely on surface aesthetics, reinforcing cultural norms, popular conventional representations and imagery. We also find that many users focus on popular topics (such as making colouring books, fantasy art, or Christmas cards), suggesting that the dominant use for the systems analysed is recreational rather than artistic.

Recent years have witnessed a rapid development of deep generative models for creating synthetic media, such as images and videos. While the practical applications of these models in everyday tasks are enticing, it is crucial to assess the inherent risks regarding their fairness. In this work, we introduce a comprehensive framework for benchmarking the performance and fairness of conditional generative models. We develop a set of metrics$\unicode{x2013}$inspired by their supervised fairness counterparts$\unicode{x2013}$to evaluate the models on their fairness and diversity. Focusing on the specific application of image upsampling, we create a benchmark covering a wide variety of modern upsampling methods. As part of the benchmark, we introduce UnfairFace, a subset of FairFace that replicates the racial distribution of common large-scale face datasets. Our empirical study highlights the importance of using an unbiased training set and reveals variations in how the algorithms respond to dataset imbalances. Alarmingly, we find that none of the considered methods produces statistically fair and diverse results.

With today's release of Chrome M121, Google announced it will introduce new generative AI features that will make the browser easier to use. The new additions will include a tab organizer, a writing assistant that helps draft text and the option to customize the artwork and themes throughout the browser. The "Experimental AI" toggle must be flipped on in the Settings page -- found in the three-dot dropdown menu -- to enable these new features. The Tab Organizer will do pretty much what it says: The built-in AI will automatically suggest ways to classify any open tabs in your Chrome windows and suggest the option to create groups. This might be helpful if you have a lot of recurring tabs open.

Artificial Intelligence (AI) is changing the world as we know it. OpenAI's ChatGPT became the fastest-growing consumer application in history, after reaching 100 million monthly active users just two months after its launch, according to a UBS study in January 2023. In higher education, the rise of AI text generators and chatbots is driving faculty and administrators to rethink their institutions' curriculum and policies. ChatGPT has raised concerns of increased opportunities for cheating and plagiarism, as well as inhibiting students' learning. A BestColleges survey found that 51% of college students agree that using AI tools on schoolwork constitutes cheating or plagiarism.

However, as of the current state of research, there appears to be a significant gap in exploratory and experimental studies specifically addressing the capabilities of Generative AI and Large Language Models (LLMs) in the context of determination and discovery of legal issues. Such studies would be instrumental in understanding how these advanced AI technologies manage the intricate task of accurately classifying and pinpointing legal matters, a domain traditionally reliant on the deep, contextual, and specialised knowledge of human legal experts. To address the identified gap in the research landscape, this study proposes an experimental and exploratory analysis of the performance of LLMs in the legal domain. The research aims to evaluate the capabilities of LLMs contrasting their performance against human legal practitioners on high volume real-world legal tasks. These types of high volume legal tasks are frequently outsourced or pushed to less experienced lawyers, and given the rapid advancements made by LLMs, raises the question of whether LLMs have achieved a level of legal comprehension that is comparable to the quality, accuracy and efficiency of Junior Lawyers or outsourced legal practitioners on such tasks.

We develop a simulation framework for studying misinformation spread within online social networks that blends agent-based modeling and natural language processing techniques. While many other agent-based simulations exist in this space, questions over their fidelity and generalization to existing networks in part hinders their ability to provide actionable insights. To partially address these concerns, we create a 'digital clone' of a known misinformation sharing network by downloading social media histories for over ten thousand of its users. We parse these histories to both extract the structure of the network and model the nuanced ways in which information is shared and spread among its members. Unlike many other agent-based methods in this space, information sharing between users in our framework is sensitive to topic of discussion, user preferences, and online community dynamics. To evaluate the fidelity of our method, we seed our cloned network with a set of posts recorded in the base network and compare propagation dynamics between the two, observing reasonable agreement across the twin networks over a variety of metrics. Lastly, we explore how the cloned network may serve as a flexible, low-cost testbed for misinformation countermeasure evaluation and red teaming analysis. We hope the tools explored here augment existing efforts in the space and unlock new opportunities for misinformation countermeasure evaluation, a field that may become increasingly important to consider with the anticipated rise of misinformation campaigns fueled by generative artificial intelligence.

We find that large language models (LLMs) are more likely to modify human-written text than AI-generated text when tasked with rewriting. This tendency arises because LLMs often perceive AI-generated text as high-quality, leading to fewer modifications. We introduce a method to detect AI-generated content by prompting LLMs to rewrite text and calculating the editing distance of the output. We dubbed our geneRative AI Detection viA Rewriting method Raidar. Raidar significantly improves the F1 detection scores of existing AI content detection models -- both academic and commercial -- across various domains, including News, creative writing, student essays, code, Yelp reviews, and arXiv papers, with gains of up to 29 points. Operating solely on word symbols without high-dimensional features, our method is compatible with black box LLMs, and is inherently robust on new content. Our results illustrate the unique imprint of machine-generated text through the lens of the machines themselves.