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.

"We've got an open consultation but that consultation is fixed and inadequate," she said. The government has proposed four options in its consultation. It describes such an outcome as "the primary object of this consultation". "Why have a preferred choice if it is an open consultation?" said Kidron. "What I say to MPs is, if you are members of a government that has put all its chips on growth, why is that same government undermining creative industries that bring 126bn to the UK economy and is giving away for free the property rights of 2.4 million people who work in those industries? The creative industries impact every constituency, region and nation," she said.

A group of investors led by Elon Musk is offering about 97.4 billion to buy the nonprofit behind OpenAI, escalating a dispute with the artificial intelligence company that Musk helped found a decade ago. Musk and his own AI startup, xAI, and a consortium of investment firms want to take control of the ChatGPT maker and revert it to its original charitable mission as a nonprofit research lab, according to Musk's attorney Marc Toberoff. OpenAI CEO Sam Altman quickly rejected the unsolicited bid on Musk's social platform X, saying, "no thank you but we will buy Twitter for 9.74 billion if you want." Musk bought Twitter, now called X, for 44 billion in 2022. Musk and Altman, who together helped start OpenAI in 2015 and later competed over who should lead it, have been in a long-running feud over the startup's direction since Musk resigned from its board in 2018.

This increased awareness has translated into guidelines, laws, and investments, such as the European Green Deal [84] or the Inflation Reduction Act in the US [106]. Many companies have used the financial incentives offered by states, and the guidelines and legislation to make significant steps towards sustainability [109]. At the same time, this growing attention also generated an advertising opportunity for companies that aim to promote themselves as environmentally aware and responsible. Indeed, some companies have been found to deliberately manipulate their data and statistics to appear more environment-friendly. The Diesel Scandal around the Volkswagen car company is a prominent example [116]. However, such cases are not the norm. More commonly, companies avoid outright data manipulation but present themselves in a misleadingly positive light regarding their environmental impact - a practice called greenwashing.

We study a fundamental challenge in the economics of innovation: an inventor must reveal details of a new idea to secure compensation or funding, yet such disclosure risks expropriation. We present a model in which a seller (inventor) and buyer (investor) bargain over an information good under the threat of hold-up. In the classical setting, the seller withholds disclosure to avoid misappropriation, leading to inefficiency. We show that trusted execution environments (TEEs) combined with AI agents can mitigate and even fully eliminate this hold-up problem. By delegating the disclosure and payment decisions to tamper-proof programs, the seller can safely reveal the invention without risking expropriation, achieving full disclosure and an efficient ex post transfer. Moreover, even if the invention's value exceeds a threshold that TEEs can fully secure, partial disclosure still improves outcomes compared to no disclosure. Recognizing that real AI agents are imperfect, we model "agent errors" in payments or disclosures and demonstrate that budget caps and acceptance thresholds suffice to preserve most of the efficiency gains. Our results imply that cryptographic or hardware-based solutions can function as an "ironclad NDA," substantially mitigating the fundamental disclosure-appropriation paradox first identified by Arrow (1962) and Nelson (1959). This has far-reaching policy implications for fostering R&D, technology transfer, and collaboration.