A day before the Senate Judiciary Committee grilled CEOs from tech companies about internet child safety, bipartisan lawmakers introduced a bill that would allow victims to sue people who create and distribute sexually-explicit deepfakes under certain circumstances. The Disrupt Explicit Forged Images and Non-Consensual Edits, or DEFIANCE Act, allows victims to sue if those who created the deepfakes knew, or "recklessly disregarded" that the victim did not consent to its making. The federal bill, introduced on Tuesday, came nearly a week after deepfake pornographic images of Taylor Swift flooded X. The social media platform temporarily removed the ability to search for Swift's name on X after the explicit content was viewed tens of millions of times. Only ten states currently have criminal laws against this form of manipulated media files.

The CEOs of five social media companies including Meta, TikTok and X (formerly Twitter) were grilled by Senators on Wednesday about how they are preventing online child sexual exploitation. The Senate Judiciary Committee called the meeting to hold the CEOs to account for what they said was a failure to prevent the abuse of minors, and ask whether they would support the laws that members of the Committee had proposed to address the problem. It is an issue that is getting worse, according to the National Center for Missing and Exploited Children, which says reports of child sexual abuse material (CSAM) reached a record high last year of more than 36 million, as reported by the Washington Post. The National Center for Missing and Exploited Children CyberTipline, a centralized system in the U.S. for reporting online CSAM, was alerted to more than 88 million files in 2022, with almost 90% of reports coming from outside the country. Mark Zuckerberg of Meta, Shou Chew of TikTok, and Linda Yaccarino of X appeared alongside Jason Spiegel of Snap and Jason Citron of Discord to answer questions from the Senate Judiciary Committee.

In 2018, Tesla awarded Elon Musk a 56 billion pay package that helped propel him to the top of world's richest lists. Now, a judge in Delaware has rendered the deal between the company and the CEO to be invalid and called the compensation an "unfathomable sum" that's unfair to shareholders. As initially seen and reported by Chancery Daily on Threads, the court of Chancery in Delaware has released its decision on the lawsuit filed by Richard Tornetta. The Tesla shareholder accused the automaker of breaching its fiduciary duty by approving a package that unjustly enriches its chief executive. Judge Kathaleen McCormick wrote in the decision that Musk "enjoyed thick ties" with the directors who were in charge of negotiating his pay package on behalf of Tesla, which means there "was no meaningful negotiation over any of the terms of the plan."

The social media platform, formerly Twitter, was so slow to react that one image racked up 47m views before it was taken down. It was largely Swift's fans who mobilised and mass-reported the images, and there was a sense of public anger, with even the White House calling it "alarming". X eventually removed the images and blocked searches to the pop star's name on Sunday evening. For women who have been victims of the creation and sharing of nonconsensual deepfake pornography, the events of the past week will have been a horrible reminder of their own abuse, even if they may also hope that the spotlight will force legislators into action. But because the pictures were removed, Swift's experience is far from the norm.

Language models have become a critical technology to tackling a wide range of natural language processing tasks, yet many details about how the best-performing language models were developed are not reported. In particular, information about their pretraining corpora is seldom discussed: commercial language models rarely provide any information about their data; even open models rarely release datasets they are trained on, or an exact recipe to reproduce them. As a result, it is challenging to conduct certain threads of language modeling research, such as understanding how training data impacts model capabilities and shapes their limitations. To facilitate open research on language model pretraining, we release Dolma, a three trillion tokens English corpus, built from a diverse mixture of web content, scientific papers, code, public-domain books, social media, and encyclopedic materials. In addition, we open source our data curation toolkit to enable further experimentation and reproduction of our work. In this report, we document Dolma, including its design principles, details about its construction, and a summary of its contents. We interleave this report with analyses and experimental results from training language models on intermediate states of Dolma to share what we have learned about important data curation practices, including the role of content or quality filters, deduplication, and multi-source mixing. Dolma has been used to train OLMo, a state-of-the-art, open language model and framework designed to build and study the science of language modeling.

The Lasso has become a benchmark data analysis procedure, and numerous variants have been proposed in the literature. Although the Lasso formulations are stated so that overall prediction error is optimized, no full control over the accuracy prediction on certain individuals of interest is allowed. In this work we propose a novel version of the Lasso in which quadratic performance constraints are added to Lasso-based objective functions, in such a way that threshold values are set to bound the prediction errors in the different groups of interest (not necessarily disjoint). As a result, a constrained sparse regression model is defined by a nonlinear optimization problem. This cost-sensitive constrained Lasso has a direct application in heterogeneous samples where data are collected from distinct sources, as it is standard in many biomedical contexts. Both theoretical properties and empirical studies concerning the new method are explored in this paper. In addition, two illustrations of the method on biomedical and sociological contexts are considered.

AI-enabled synthetic biology has tremendous potential but also significantly increases biorisks and brings about a new set of dual use concerns. The picture is complicated given the vast innovations envisioned to emerge by combining emerging technologies, as AI-enabled synthetic biology potentially scales up bioengineering into industrial biomanufacturing. However, the literature review indicates that goals such as maintaining a reasonable scope for innovation, or more ambitiously to foster a huge bioeconomy don't necessarily contrast with biosafety, but need to go hand in hand. This paper presents a literature review of the issues and describes emerging frameworks for policy and practice that transverse the options of command-and control, stewardship, bottom-up, and laissez-faire governance. How to achieve early warning systems that enable prevention and mitigation of future AI-enabled biohazards from the lab, from deliberate misuse, or from the public realm, will constantly need to evolve, and adaptive, interactive approaches should emerge. Although biorisk is subject to an established governance regime, and scientists generally adhere to biosafety protocols, even experimental, but legitimate use by scientists could lead to unexpected developments. Recent advances in chatbots enabled by generative AI have revived fears that advanced biological insight can more easily get into the hands of malignant individuals or organizations. Given these sets of issues, society needs to rethink how AI-enabled synthetic biology should be governed. The suggested way to visualize the challenge at hand is whack-a-mole governance, although the emerging solutions are perhaps not so different either.

In many causal inference applications, the primary outcomes are missing for a non-trivial number of observations. For instance, in studies on long-term health effects of medical interventions, some measurements require expensive testing and a loss to follow-up is common (Hogan et al., 2004). In evaluating commercial online ad effectiveness, some individuals may drop out from the panel because they use multiple devices (Shankar et al., 2023), leading to missing revenue measures. In many of these studies, however, there often exist short-term outcomes that are easier and faster to measure, e.g., short-term health measures or an online ad's click-through rate, that are observed for a greater share of the sample. These outcomes, which are typically informative about the primary outcomes themselves, are refered to as surrogate outcomes or surrogates. There is a rich causal inference literature addressing missing outcome data. Simply restricting to data with observed primary outcomes may induce strong bias (Hernán and Robins, 2010). Ignoring unlabeled data also reduces the effective sample size for estimating the treatment effects and inflates the variance. Chakrabortty et al. (2022) considered the missing completely at random (MCAR) setting and showed that incorporating unlabeled data reduces variance.

The objective of legal text entailment is to ascertain whether the assertions in a legal query logically follow from the information provided in one or multiple legal articles. ChatGPT, a large language model, is robust in many natural language processing tasks, including legal text entailment: when we set the temperature = 0 (the ChatGPT answers are deterministic) and prompt the model, it achieves 70.64% accuracy on COLIEE 2022 dataset, which outperforms the previous SOTA of 67.89%. On the other hand, if the temperature is larger than zero, ChatGPT answers are not deterministic, leading to inconsistent answers and fluctuating results. We propose to leverage label models (a fundamental component of weak supervision techniques) to integrate the provisional answers by ChatGPT into consolidated labels. By that way, we treat ChatGPT provisional answers as noisy predictions which can be consolidated by label models. The experimental results demonstrate that this approach can attain an accuracy of 76.15%, marking a significant improvement of 8.26% over the prior state-of-the-art benchmark. Additionally, we perform an analysis of the instances where ChatGPT produces incorrect answers, then we classify the errors, offering insights that could guide potential enhancements for future research endeavors.

Recent advances in deep generative models have greatly expanded the potential to create realistic synthetic health datasets. These synthetic datasets aim to preserve the characteristics, patterns, and overall scientific conclusions derived from sensitive health datasets without disclosing patient identity or sensitive information. Thus, synthetic data can facilitate safe data sharing that supports a range of initiatives including the development of new predictive models, advanced health IT platforms, and general project ideation and hypothesis development. However, many questions and challenges remain, including how to consistently evaluate a synthetic dataset's similarity and predictive utility in comparison to the original real dataset and risk to privacy when shared. Additional regulatory and governance issues have not been widely addressed. In this primer, we map the state of synthetic health data, including generation and evaluation methods and tools, existing examples of deployment, the regulatory and ethical landscape, access and governance options, and opportunities for further development.