By filtering the content that users see, social media platforms have the ability to influence users' perceptions and decisions, from their dining choices to their voting preferences. This influence has drawn scrutiny, with many calling for regulations on filtering algorithms, but designing and enforcing regulations remains challenging. In this work, we examine three questions. First, given a regulation, how would one design an audit to enforce it? Second, does the audit impose a performance cost on the platform?

Plus: Spy firms tap into a global telecom weakness to track targets, 500,000 UK health records go up for sale on Alibaba, Apple patches a revealing notification bug, and more. As researchers and practitioners debate the impact that new AI models will have on cybersecurity, Mozilla said on Tuesday it used early access to Anthropic's Mythos Preview to find and fix 271 vulnerabilities in its new Firefox 150 browser release. Meanwhile, researchers identified a group of moderately successful North Korean hackers using AI for everything from vibe coding malware to creating fake company websites--stealing up to $12 million in three months. Researchers have finally cracked disruptive malware known as Fast16 that predates Stuxnet and may have been used to target Iran's nuclear program. It was created in 2005 and was likely deployed by the US or an ally.

Although the fairness community has recognized the importance of data, re-searchers in the area primarily rely on UCIAdult when it comes to tabular data. Derived from a 1994 USCensus survey, this dataset has appeared in hundreds of research papers where it served as the basis for the development and comparison of many algorithmic fairness interventions. We reconstruct a superset of the UCI Adult data from available USCensus sources and reveal idiosyncrasies of the UCIAdult dataset that limit its external validity. Our primary contribution is asuite of new datasets derived from USCensus surveys that extend the existing data ecosystem for research on fair machine learning. We create prediction tasks relating to income, employment, health, transportation, and housing. The data span multiple years and all states of the United States, allowing researchers to studytemporal shift and geographic variation. We highlight a broad initial sweep of new empirical insights relating to trade-offs between fairness criteria, performance of algorithmic interventions, and the role of distribution shift based on our new datasets. Our findings inform ongoing debates, challenge some existing narratives, and point to future research directions.

A Justice Ministry panel discusses how the voices of individuals should be protected under publicity and portrait rights, amid a rise in the unauthorized use of celebrities' voices by generative artificial intelligence, at the ministry in Tokyo on Friday. An expert panel under the Justice Ministry has agreed that the voices of individuals should be protected under publicity and portrait rights, amid a rise in the unauthorized use of celebrities' voices by generative artificial intelligence. The agreement was made Friday, during the first meeting of the panel on civil compensation claims related to the unauthorized use of celebrities' images and voices by generative AI. The ministry is set to compile guidelines on the scope and standards for illegal acts under current law by this summer. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

We provide practical, efficient, and nonparametric methods for auditing the fairness of deployed classification and regression models. Whereas previous work relies on a fixed-sample size, our methods are sequential and allow for the continuous monitoring of incoming data, making them highly amenable to tracking the fairness of real-world systems. We also allow the data to be collected by a probabilistic policy as opposed to sampled uniformly from the population. This enables auditing to be conducted on data gathered for another purpose. Moreover, this policy may change over time and different policies may be used on different subpopulations. Finally, our methods can handle distribution shift resulting from either changes to the model or changes in the underlying population. Our approach is based on recent progress in anytime-valid inference and game-theoretic statistics--the "testing by betting" framework in particular. These connections ensure that our methods are interpretable, fast, and easy to implement. We demonstrate the efficacy of our approach on three benchmark fairness datasets.

More precisely, we use batches of size 2. Each batch contains one patch with the foreground oversampled. Furthermore, we split each silo's data into training and validation data with 80% and 20% split, respectively. All this pre-processing and patching is done using the nnU-Net library [IJK+21]. Loss function We use the same loss function as proposed by nnU-Net [IJK+21] for the KiTS19 dataset which is based on DICE [Dic45] and on the Cross Entropy loss.

OpenAI's Sam Altman apologises over failure to report Canadian mass shooter OpenAI CEO Sam Altman has apologised over his company's failure to warn authorities about the concerning online activities of a teen who went on to commit one of Canada's worst mass shooting s. Jesse Van Rootselaar, 18, went on a shooting spree in Tumbler Ridge, British Columbia, on February 10, killing eight people. Rootselaar, who was born male but identified as female, died of a self-inflicted gunshot wound. OpenAI said after the attacks that Rootselaar's ChatGPT account had been flagged internally the previous June for misuse "in furtherance of violent activities", resulting in its suspension. The San Francisco-based AI company said at the time that it had not informed authorities, as Rootselaar's usage of the chatbot had not met the threshold of posing a credible or imminent threat of harm to others.