It is not only OpenAI but the AI race itself that was vindicated in the California courtroom last night . Even though Elon Musk essentially lost on a technicality, there's a clear signal from the verdict that making lots of money from AI and competing fiercely with rivals is simply business. The industry sometimes tries to display a united front, especially when it comes to safety, research and inclusivity. But this case served as a powerful reminder that none of the AI giants are charities and don't have to be, even if they once said otherwise. Cracks in the façade of industry collaboration for the sake of humanity have been exposed before.

The US Built a Site to Ensure Fair Access to Public Lands. Recreation.gov was supposed to make access to public lands more equitable and streamlined. It's a few minutes before 8 am Mountain Time on March 16, the day that river permit cancellations are released on Recreation.gov, the federal website for public land reservations. Rec.gov, as it's commonly called, administers everything from river permits and timed entrance fees at the most popular national parks to campground reservations on remote sites belonging to the Bureau of Land Management, and a lot of people are recreating on public land these days. There were 11 million reservations on the site in 2024, up significantly from 3.5 million reservations reported in 2019. At the center of it all is an unlikely player in the outdoor recreation space: The site is operated by the government contractor Booz Allen Hamilton, a corporation known more for cybersecurity than rafting trips. Early each year, outdoor enthusiasts gear up for Recreation.gov's annual lotteries for some of the most iconic experiences in the country: a river trip down Idaho's Middle Fork of the Salmon River, which flows through the Frank Church River of No Return Wilderness. Backcountry permits to hike into the Wave, an otherworldly rock formation in Arizona's Paria Canyon-Vermilion Cliffs Wilderness. Overnight stays in the rugged, lake-studded Enchantments, in Washington's Okanogan-Wenatchee National Forest. Odds of getting a desirable Middle Fork permit are around 2 percent.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Alberto Carvalho sits with third-grade students as he visits classrooms at Lenicia B. Weemes Elementary School on the first day of classes for LAUSD students in 2023. This is read by an automated voice. Please report any issues or inconsistencies here . Leaders who helped drive L.A. Unified's recent test-score gains are exiting as Supt.

The Enrollment Cliff Is Here. Which Schools Will Survive It? As the number of new high-school graduates drops, colleges will close, some will merge, and others may change beyond recognition. This series on the future of higher education started with a simple question: Should I still be contributing to my children's college funds? My first attempt to answer that question centered on the growing disillusionment with higher education in general.

Starting May 19, tech platforms in the US will have to start complying with the Take It Down Act. Here's how more than a dozen of the largest platforms are handling takedown demands for your nudes. Abstract collage illustration of woman face partially obscured by a glitching pixelated effect on a green background. Starting on Tuesday, May 19, tech platforms have to provide a way for people to report nonconsensual intimate images and videos, or NCII, uploaded to their platforms. The new requirement is thanks to the Take It Down Act, a law backed by First Lady Melania Trump that passed last year with bipartisan support.

The wars in Ukraine and Iran reveal a structural shift in modern warfare where the final ten percent of military degradation costs more than the first ninety percent.

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Standard Chartered said it would cut 15% of its corporate function roles by 2030. Standard Chartered said it would cut 15% of its corporate function roles by 2030. Standard Chartered plans to cut more than 7,000 jobs over the next four years as it increasingly uses artificial intelligence. The London-headquartered lender is one of the first major global banks to lay out plans to cut thousands of jobs, citing AI as a driver to make its operations slimmer as it seeks to increase its profitability and tackle competition. StanChart said on Tuesday it would cut 15% of its back-office roles by 2030, which would result in about 7,800 redundancies out of its more than 52,000 staff in such roles.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' A Russian attack has damaged port infrastructure in Ukraine's Danube River port city of Izmail, a vital grain-export hub, while Russian authorities said they had downed four Ukrainian drones headed towards Moscow, as peace efforts remain stalled and both sides continue reciprocal attacks. Izmail, in the Odesa region, is a frequently targeted logistical centre and was hit in the early hours of Tuesday. It is Ukraine's largest port on the Danube. The attack lasted from about 1am to 3am (22:00 to 00:00 GMT), with firefighters battling a blaze in a building with blown-out windows. This followed another Russian attack on port infrastructure in Izmail on the night of May 2. In Kharkiv, two people were rescued, and one may remain trapped under the rubble after a Russian drone attack, Mayor Ihor Terekhov said on Telegram.

Automated systems built on artificial intelligence (AI) are increasingly deployed across high-stakes domains, raising critical concerns about fairness and the perpetuation of demographic disparities that exist in the world. In this context, causal inference provides a principled framework for reasoning about fairness, as it links observed disparities to underlying mechanisms and aligns naturally with human intuition and legal notions of discrimination. Prior work on causal fairness primarily focuses on the standard machine learning setting, where a decision-maker constructs a single predictive mechanism $f_{\widehat Y}$ for an outcome variable $Y$, while inheriting the causal mechanisms of all other covariates from the real world. The generative AI setting, however, is markedly more complex: generative models can sample from arbitrary conditionals over any set of variables, implicitly constructing their own beliefs about all causal mechanisms rather than learning a single predictive function. This fundamental difference requires new developments in causal fairness methodology. We formalize the problem of causal fairness in generative AI and unify it with the standard ML setting under a common theoretical framework. We then derive new causal decomposition results that enable granular quantification of fairness impacts along both (a) different causal pathways and (b) the replacement of real-world mechanisms by the generative model's mechanisms. We establish identification conditions and introduce efficient estimators for causal quantities of interest, and demonstrate the value of our methodology by analyzing race and gender bias in large language models across different datasets.