Striking an optimal balance between predictive performance and fairness continues to be a fundamental challenge in machine learning. In this work, we propose a post-processing framework that facilitates fairness-aware prediction by leveraging model ensembling. Designed to operate independently of any specific model internals, our approach is widely applicable across various learning tasks, model architectures, and fairness definitions. Through extensive experiments spanning classification, regression, and survival analysis, we demonstrate that the framework effectively enhances fairness while maintaining, or only minimally affecting, predictive accuracy.

Online social platforms increasingly rely on crowd-sourced systems to label misleading content at scale, but these systems must both aggregate users' evaluations and decide whose evaluations to trust. To address the latter, many platforms audit users by rewarding agreement with the final aggregate outcome, a design we term consensus-based auditing. We analyze the consequences of this design in X's Community Notes, which in September 2022 adopted consensus-based auditing that ties users' eligibility for participation to agreement with the eventual platform outcome. We find evidence of strategic conformity: minority contributors' evaluations drift toward the majority and their participation share falls on controversial topics, where independent signals matter most. We formalize this mechanism in a behavioral model in which contributors trade off private beliefs against anticipated penalties for disagreement. Motivated by these findings, we propose a two-stage auditing and aggregation algorithm that weights contributors by the stability of their past residuals rather than by agreement with the majority. The method first accounts for differences across content and contributors, and then measures how predictable each contributor's evaluations are relative to the latent-factor model. Contributors whose evaluations are consistently informative receive greater influence in aggregation, even when they disagree with the prevailing consensus. In the Community Notes data, this approach improves out-of-sample predictive performance while avoiding penalization of disagreement.

Prediction-powered inference (PPI) is a rapidly growing framework for combining machine learning predictions with a small set of gold-standard labels to conduct valid statistical inference. In this article, I argue that the core estimators underlying PPI are equivalent to well-established estimators from the survey sampling literature dating back to the 1970s. Specifically, the PPI estimator for a population mean is algebraically equivalent to the difference estimator of Cassel et al. (1976), and PPI plus corresponds to the generalized regression (GREG) estimator of Sarndal et al. (2003). Recognizing this equivalence, I consider what part of PPI is inherited from a long-standing literature in statistics, what part is genuinely new, and where inferential claims require care. After introducing the two frameworks and establishing their equivalence, I break down where PPI diverges from model-assisted estimation, including differences in the mode of inference, the role of the unlabeled data pool, and the consequences of differential prediction error for subgroup estimands such as the average treatment effect. I then identify what each framework offers the other: PPI researchers can draw on the survey sampling literature's well-developed theory of calibration, optimal allocation, and design-based diagnostics, while survey sampling researchers can benefit from PPI's extensions to non-standard estimands and its accessible software ecosystem. The article closes with a call for integration between these two communities, motivated by the growing use of large language models as measurement instruments in applied research.

Global flash floods, exacerbated by climate change, pose severe threats to humanlife, infrastructure, and the environment. Recent catastrophic events in Pakistan andNew Zealand underscore the urgent need for precise flood mapping to guide restoration efforts, understand vulnerabilities, and prepare for future occurrences. While Synthetic Aperture Radar (SAR) remote sensing offers day-and-night, all-weatherimaging capabilities, its application in deep learning for flood segmentation is limited by the lack of large annotated datasets. To address this, we introduce KuroSiwo, a manually annotated multi-temporal dataset, spanning 43 flood events globally. Our dataset maps more than 338 billion $m^2$ of land, with 33 billion designatedas either flooded areas or permanent water bodies. Kuro Siwo includes a highlyprocessed product optimized for flash flood mapping based on SAR Ground RangeDetected, and a primal SAR Single Look Complex product with minimal preprocessing, designed to promote research on the exploitation of both the phase and amplitude information and to offer maximum flexibility for downstream task preprocessing. To leverage advances in large scale self-supervised pretraining methodsfor remote sensing data, we augment Kuro Siwo with a large unlabeled set of SARsamples. Finally, we provide an extensive benchmark, namely BlackBench, offering strong baselines for a diverse set of flood events globally.

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Ros Atkins on... Trump's mixed messages on the war For every day of this war, President Trump has been sharing his perspective and his thinking - whether in press conferences, in video statements or in posts on social media. In the last week, that's continued - as strikes have been exchanged - and pressure has built on the supply of oil and gas from the region. The BBC's Analysis Editor Ros Atkins has looked at what the President's been saying. Watch: Sean Penn receives'Oscar' in Ukraine after skipping US ceremony The Academy Award winning US actor won his third Oscar on Sunday, but skipped the ceremony to visit Ukraine. Voiced by Domhnall Gleeson and directed by John Kelly, Retirement Plan is nominated for Best Animated Short Film at the 98th Academy Awards.

Stay up to date with the Fox News AI Newsletter as the U.S. Navy plans to adopt robots that climb wall of warships and Dell announces plans to shrink its workforce.

Last year, as FCC chair Brendan Carr threatened ABC over a Jimmy Kimmel monologue, a civil servant overseeing West Coast stations privately pledged support, according to emails obtained by WIRED. A senior Federal Communications Commission official overseeing ABC-owned California stations privately offered to assist FCC Chairman Brendan Carr's campaign last year against the Walt Disney Co. and, according to internal emails obtained by WIRED. On September 17, Carr threatened Disney with regulatory action regarding the Jimmy Kimmel monologue about the assassination of Charlie Kirk, prompting major station affiliates to drop the broadcast and forcing ABC to temporarily suspend the show. The email, obtained via the Freedom of Information Act, was titled "personal note of support re Charlie Kirk ABC/Disney issue" and quoted Carr's remarks from an interview with conservative podcaster Benny Johnson: "This is a very, very serious issue right now for Disney. We can do this the easy way or the hard way," Carr said during the interview.

In a meeting with Japanese Prime Minister Sanae Takaichi in the Oval Office, US President Donald Trump was asked why he didn't inform allies about his plan to attack Iran. Trump responded by raising Japan's attack on Pearl Harbor during World War II, saying, Who knows better about surprise than Japan? Watch: Sean Penn receives'Oscar' in Ukraine after skipping US ceremony The Academy Award winning US actor won his third Oscar on Sunday, but skipped the ceremony to visit Ukraine. Voiced by Domhnall Gleeson and directed by John Kelly, Retirement Plan is nominated for Best Animated Short Film at the 98th Academy Awards. 'I don't know why we're doing it' - Americans divided on Iran war Ten days since President Trump first announced the attack, people from across the US tell the BBC what they think the best outcome of the conflict could be.