Recent text-based causal methods attempt to mitigate confounding bias by including unstructured text data as proxies of confounding variables that are partially or imperfectly measured. These approaches assume analysts have supervised labels of the confounders given text for a subset of instances, a constraint that is not always feasible due to data privacy or cost. Here, we address settings in which an important confounding variable is completely unobserved. We propose a new causal inference method that splits pre-treatment text data, infers two proxies from two zero-shot models on the separate splits, and applies these proxies in the proximal g-formula. We prove that our text-based proxy method satisfies identification conditions required by the proximal g-formula while other seemingly reasonable proposals do not. We evaluate our method in synthetic and semi-synthetic settings and find that it produces estimates with low bias. This combination of proximal causal inference and zero-shot classifiers is novel (to our knowledge) and expands the set of text-specific causal methods available to practitioners.

Personalization in machine learning (ML) tailors models' decisions to the individual characteristics of users. While this approach has seen success in areas like recommender systems, its expansion into high-stakes fields such as healthcare and autonomous driving is hindered by the extensive regulatory approval processes involved. To address this challenge, we propose a novel framework termed represented Markov Decision Processes (r-MDPs) that is designed to balance the need for personalization with the regulatory constraints. In an r-MDP, we cater to a diverse user population, each with unique preferences, through interaction with a small set of representative policies. Our objective is twofold: efficiently match each user to an appropriate representative policy and simultaneously optimize these policies to maximize overall social welfare. We develop two deep reinforcement learning algorithms that efficiently solve r-MDPs. These algorithms draw inspiration from the principles of classic K-means clustering and are underpinned by robust theoretical foundations. Our empirical investigations, conducted across a variety of simulated environments, showcase the algorithms' ability to facilitate meaningful personalization even under constrained policy budgets. Furthermore, they demonstrate scalability, efficiently adapting to larger policy budgets.

Reinforcement Learning from Human Feedback (RLHF) has become a crucial technology for aligning language models with human values and intentions, enabling models to produce more helpful and harmless responses. Reward models are trained as proxies for human preferences to drive reinforcement learning optimization. While reward models are often considered central to achieving high performance, they face the following challenges in practical applications: (1) Incorrect and ambiguous preference pairs in the dataset may hinder the reward model from accurately capturing human intent. (2) Reward models trained on data from a specific distribution often struggle to generalize to examples outside that distribution and are not suitable for iterative RLHF training. In this report, we attempt to address these two issues. (1) From a data perspective, we propose a method to measure the strength of preferences within the data, based on a voting mechanism of multiple reward models. Experimental results confirm that data with varying preference strengths have different impacts on reward model performance. We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset and fully leverage high-quality preference data. (2) From an algorithmic standpoint, we introduce contrastive learning to enhance the ability of reward models to distinguish between chosen and rejected responses, thereby improving model generalization. Furthermore, we employ meta-learning to enable the reward model to maintain the ability to differentiate subtle differences in out-of-distribution samples, and this approach can be utilized for iterative RLHF optimization.

Machine learning models can perpetuate unintended biases from unfair and imbalanced datasets. Evaluating and debiasing these datasets and models is especially hard in text datasets where sensitive attributes such as race, gender, and sexual orientation may not be available. When these models are deployed into society, they can lead to unfair outcomes for historically underrepresented groups. In this paper, we present a dataset coupled with an approach to improve text fairness in classifiers and language models. We create a new, more comprehensive identity lexicon, TIDAL, which includes 15,123 identity terms and associated sense context across three demographic categories. We leverage TIDAL to develop an identity annotation and augmentation tool that can be used to improve the availability of identity context and the effectiveness of ML fairness techniques. We evaluate our approaches using human contributors, and additionally run experiments focused on dataset and model debiasing. Results show our assistive annotation technique improves the reliability and velocity of human-in-the-loop processes. Our dataset and methods uncover more disparities during evaluation, and also produce more fair models during remediation. These approaches provide a practical path forward for scaling classifier and generative model fairness in real-world settings.

And because a LLM doesn't "know" when it's quoting from training data, there's no obvious way to prevent the behavior. I spoke with Florian Tramèr, a prominent AI-security researcher and co-author of some of the above studies. It's "an extremely tricky problem to study," he told me. "It's very, very hard to pin down a good definition of memorization." One way to understand the concept is to think of an LLM as an enormous decision tree in which each node is an English word. From a given starting word, an LLM chooses the next word from the entire English vocabulary.

Harvard University offers a behemoth of courses that teach its students topics including "Queering Education," "Black Radicalism" and sexual fetishes. However, its course catalog – while offering many topics some would consider strongly critical of America – shows it does not offer significant courses focusing on American patriotism in depth despite taking in hundreds of millions of taxpayer dollars every year. In 2021, Harvard received 625 million from American taxpayers, all the while the Ivy League boasts over 50 billion in its endowment. Some companies and prospective students are starting to question their interest in Harvard, particularly after scandals relating to alleged pervasive antisemitism and pro-Hamas sentiment on its campus – prompting legal action and a civil rights investigation from the U.S. Department of Education. Harvard's education department for prospective K-12 teachers elaborates on how one can bring queerness and transgenderism into schools.

Judges in England and Wales will have approval for "careful use" of artificial intelligence (AI) to help produce rulings, but experts remain divided over how extensively judges or the wider law profession should seek to use the technology. "I would say AI is probably appropriate to cast a wide net to gather as much information as possible," William A. Jacobson, a Cornell University Law professor and founder of the Equal Protection Project, told Fox News Digital. "That might inform your decision, but I don't think it is at a place now – and I don't know if it ever will be – that it can actually do the sorting … and make the sort of decisions and determinations that you need to make, whether it's as a judge or a lawyer," Jacobson said. The Courts and Tribunals Judiciary, the body of various judges, magistrates, tribunal members and coroners in England and Wales, decided that judges may use AI to write opinions, and only opinions, with no leeway to use the technology for research or legal analyses due to the potential for AI to fabricate information and provide misleading, inaccurate and biased information. Caution over AI's use in the legal field partially stems from a few high-profile blunders that resulted from lawyers experimenting with the tech, which produced court filings that included references to fictional cases, known as "hallucinations."

This is a staggeringly difficult problem to solve as current AI cannot look at a blank canvas and create something original,

Art that can be easily replicated by artificial intelligence is "meaningless", according to the Chinese dissident artist Ai Weiwei, who believes even Pablo Picasso and Henri Matisse would have had to rethink their approach if AI had existed in their era. Ai Weiwei's comments feed into the current charged debate about the rise of AIs that use data scraped from artists' websites to create "original" images in their style. There have been several class-action lawsuits in the US, and artists whose aesthetic is popular among users of AIs have already reported thousands of images that use their work as a base, often without permission. When asked about the issue, Ai Weiwei said: "That's not a problem. I think that kind of art should [have died] a long time ago," before he criticised art teaching that focuses on creating "realistic" images.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Lee made the announcement while standing in the middle of Nashville's famed RCA Studio A, a location where legends such as Dolly Parton, Willie Nelson and Charley Pride have all recorded. Packed inside were top music industry leaders, songwriters and lawmakers, all eager to praise the state's rich musical history while also sounding the alarm about the threats AI poses. "Tennessee will be the first state in the country to protect artists' voices with this legislation," Lee said. "And we hope it will be a blueprint for the country."