Google's executives gave details on Wednesday on how the tech giant will sunset its diversity initiatives and defended dropping its pledge against building artificial intelligence for weaponry and surveillance in an all-staff meeting. Melonie Parker, Google's former head of diversity, said the company was doing away with its diversity and inclusion employee training programs and "updating" broader training programs that have "DEI content". It was the first time company executives have addressed the whole staff since Google announced it would no longer follow hiring goals for diversity and took down its pledge not to build militarized AI. The chief legal officer, Kent Walker, said a lot had changed since Google first introduced its AI principles in 2018, which explicitly stated Google would not build AI for harmful purposes. He said it would be "good for society" for the company to be part of evolving geopolitical discussions in response to a question about why the company removed prohibitions against building AI for weapons and surveillance.

The United States and United Kingdom have refused to sign an Artificial Intelligence Action Summit declaration calling for policies "ensuring AI is open, inclusive, transparent, ethical, safe, secure and trustworthy". The summit in Paris on Monday and Tuesday brought together representatives from more than 100 countries to discuss how to reach a consensus on guiding the development of AI. "We are still in the early days, and I already believe AI will be the most profound shift of our lifetimes," Google CEO Sundar Pichai told the summit. The meeting, which was held amid a three-way race for AI dominance, revealed a divide in the priorities of some nations. While Europe is seeking to regulate and invest, China is focused on expanding access through state-backed tech giants, and the US is pushing for a hands-off approach in terms of regulation. Here's what you need to know about the summit and the AI race: Some leaders at the summit emphasised the need for the creation of a diverse and inclusive AI "ecosystem" that is human rights-based, ethical, safe and trustworthy.

The two had a blowout argument over the future direction of OpenAI – the company they came together to found in 2015 – with Altman seemingly content to pursue a for-profit approach and Musk feeling that was forswearing the founding principles of the firm as well as its name. OpenAI couldn't be open, he reckoned, if it was closed off and trying to make money rather than better humanity. So it's no surprise that Musk, who lodged an audacious bid to take over Twitter a little more than two years ago, which ended up with his ownership of the platform now called X, has sought to put a spoiler in two years of near-untrammelled growth for OpenAI. Musk – who is currently overhauling (to his supporters; "tearing down" to his opponents) the US government to be, as he would describe it, leaner and more efficient while also devastating important programmes such as international aid and cutting-edge scientific research – has lodged a near 100bn bid for OpenAI's non-profit arm. "It's time for OpenAI to return to the open-source, safety-focused force for good it once was," Musk said in a statement supplied by the lawyer shepherding his bid.

Understanding human behavior and society is a central focus in social sciences, with the rise of generative social science marking a significant paradigmatic shift. By leveraging bottom-up simulations, it replaces costly and logistically challenging traditional experiments with scalable, replicable, and systematic computational approaches for studying complex social dynamics. Recent advances in large language models (LLMs) have further transformed this research paradigm, enabling the creation of human-like generative social agents and realistic simulacra of society. In this paper, we propose AgentSociety, a large-scale social simulator that integrates LLM-driven agents, a realistic societal environment, and a powerful large-scale simulation engine. Based on the proposed simulator, we generate social lives for over 10k agents, simulating their 5 million interactions both among agents and between agents and their environment. Furthermore, we explore the potential of AgentSociety as a testbed for computational social experiments, focusing on four key social issues: polarization, the spread of inflammatory messages, the effects of universal basic income policies, and the impact of external shocks such as hurricanes. These four issues serve as valuable cases for assessing AgentSociety's support for typical research methods -- such as surveys, interviews, and interventions -- as well as for investigating the patterns, causes, and underlying mechanisms of social issues. The alignment between AgentSociety's outcomes and real-world experimental results not only demonstrates its ability to capture human behaviors and their underlying mechanisms, but also underscores its potential as an important platform for social scientists and policymakers.

To help users make privacy-related decisions, personalized privacy assistants based on AI technology have been developed in recent years. These AI-driven Personalized Privacy Assistants (AI-driven PPAs) can reap significant benefits for users, who may otherwise struggle to make decisions regarding their personal data in environments saturated with privacy-related decision requests. However, no study systematically inquired about the features of these AI-driven PPAs, their underlying technologies, or the accuracy of their decisions. To fill this gap, we present a Systematization of Knowledge (SoK) to map the existing solutions found in the scientific literature. We screened 1697 unique research papers over the last decade (2013-2023), constructing a classification from 39 included papers. As a result, this SoK reviews several aspects of existing research on AI-driven PPAs in terms of types of publications, contributions, methodological quality, and other quantitative insights. Furthermore, we provide a comprehensive classification for AI-driven PPAs, delving into their architectural choices, system contexts, types of AI used, data sources, types of decisions, and control over decisions, among other facets. Based on our SoK, we further underline the research gaps and challenges and formulate recommendations for the design and development of AI-driven PPAs as well as avenues for future research.

Large language models (LLMs) are helping millions of users write texts about diverse issues, and in doing so expose users to different ideas and perspectives. This creates concerns about issue bias, where an LLM tends to present just one perspective on a given issue, which in turn may influence how users think about this issue. So far, it has not been possible to measure which issue biases LLMs actually manifest in real user interactions, making it difficult to address the risks from biased LLMs. Therefore, we create IssueBench: a set of 2.49m realistic prompts for measuring issue bias in LLM writing assistance, which we construct based on 3.9k templates (e.g. "write a blog about") and 212 political issues (e.g. "AI regulation") from real user interactions. Using IssueBench, we show that issue biases are common and persistent in state-of-the-art LLMs. We also show that biases are remarkably similar across models, and that all models align more with US Democrat than Republican voter opinion on a subset of issues. IssueBench can easily be adapted to include other issues, templates, or tasks. By enabling robust and realistic measurement, we hope that IssueBench can bring a new quality of evidence to ongoing discussions about LLM biases and how to address them.

Safety concerns are out, optimism is in: that was the takeaway from a major artificial intelligence summit in Paris this week, as leaders from the U.S., France, and beyond threw their weight behind the AI industry. Although there were divisions between major nations--the U.S. and the U.K. did not sign a final statement endorsed by 60 nations calling for an "inclusive" and "open" AI sector--the focus of the two-day meeting was markedly different from the last such gathering. Last year, in Seoul, the emphasis was on defining red-lines for the AI industry. The concern: that the technology, although holding great promise, also had the potential for great harm. The final statement made no mention of significant AI risks nor attempts to mitigate them, while in a speech on Tuesday, U.S. Vice President J.D. Vance said: "I'm not here this morning to talk about AI safety, which was the title of the conference a couple of years ago. I'm here to talk about AI opportunity."

In the complaint, Thomson Reuters claimed the AI firm reproduced materials from its legal research firm Westlaw. "None of Ross's possible defenses holds water. I reject them all," wrote US District Court of Delaware judge Stephanos Bibas, in a summary judgement. Thomson Reuters and Ross Intelligence did not immediately respond to requests for comment. Right now, there are several dozen lawsuits currently winding through the US court system, as well as international challenges in China, Canada, the UK, and other countries. Notably, Judge Bibas ruled in Thomson Reuters' favor on the question of fair use.

As with an invasive species, crawlers for AI have an insatiable and undiscerning appetite for data, hoovering up Wikipedia articles, academic papers, and posts on Reddit, review websites, and blogs. All forms of data are on the menu--text, tables, images, audio, and video. And the AI systems that result can (but not always will) be used in ways that compete directly with their sources of data. News sites fear AI chatbots will lure away their readers; artists and designers fear that AI image generators will seduce their clients; and coding forums fear that AI code generators will supplant their contributors. In response, websites are starting to turn crawlers away at the door.

United States Vice President JD Vance has warned against "excessive regulation" of artificial intelligence at a Paris summit on the technology, warning both European allies and rivals like China against tightening governmental grip. "Excessive regulation of the AI sector could kill a transformative sector just as it's taking off," Vance told global leaders, tech industry chiefs and policymakers gathered on Tuesday at the French capital's Grand Palais. A three-way race for AI dominance has emerged at the summit, with Europe seeking to regulate and invest, China expanding access through state-backed tech giants and the US, under President Donald Trump, championing a hands-off approach. In a thinly veiled jab against China, Vance also warned global leaders against striking artificial intelligence deals with "authoritarian regimes". "Partnering with them means chaining your nation to an authoritarian master that seeks to infiltrate, dig in and seize your information infrastructure," he said.