Fair regression methods have the potential to mitigate societal bias concerns in health care, but there has been little work on penalized fair regression when multiple groups experience such bias. We propose a general regression framework that addresses this gap with unfairness penalties for multiple groups. Our approach is demonstrated for binary outcomes with true positive rate disparity penalties. It can be efficiently implemented through reduction to a cost-sensitive classification problem. We additionally introduce novel score functions for automatically selecting penalty weights. Our penalized fair regression methods are empirically studied in simulations, where they achieve a fairness-accuracy frontier beyond that of existing comparison methods. Finally, we apply these methods to a national multi-site primary care study of chronic kidney disease to develop a fair classifier for end-stage renal disease. There we find substantial improvements in fairness for multiple race and ethnicity groups who experience societal bias in the health care system without any appreciable loss in overall fit.

As frontier artificial intelligence (AI) models rapidly advance, benchmarks are integral to comparing different models and measuring their progress in different task-specific domains. However, there is a lack of guidance on when and how benchmarks should be deprecated once they cease to effectively perform their purpose. This risks benchmark scores over-valuing model capabilities, or worse, obscuring capabilities and safety-washing. Based on a review of benchmarking practices, we propose criteria to decide when to fully or partially deprecate benchmarks, and a framework for deprecating benchmarks. Our work aims to advance the state of benchmarking towards rigorous and quality evaluations, especially for frontier models, and our recommendations are aimed to benefit benchmark developers, benchmark users, AI governance actors (across governments, academia, and industry panels), and policy makers.

Topic model and document-clustering evaluations either use automated metrics that align poorly with human preferences or require expert labels that are intractable to scale. We design a scalable human evaluation protocol and a corresponding automated approximation that reflect practitioners' real-world usage of models. Annotators -- or an LLM-based proxy -- review text items assigned to a topic or cluster, infer a category for the group, then apply that category to other documents. Using this protocol, we collect extensive crowdworker annotations of outputs from a diverse set of topic models on two datasets. We then use these annotations to validate automated proxies, finding that the best LLM proxies are statistically indistinguishable from a human annotator and can therefore serve as a reasonable substitute in automated evaluations. Package, web interface, and data are at https://github.com/ahoho/proxann

Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Elections. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to track answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at \url{https://wave-pulse.io}.

True randomness is for examining how humans generate sequences devoid of predictable incredibly hard to generate artificially [48], and most computergenerated patterns. By adapting an existing human RNGT for an random number generations (RNGs) employed in these LLM-compatible environment, this preliminary study tests whether tasks are actually pseudorandom rather than truly random [11, 25]. ChatGPT-3.5, a large language model (LLM) trained on humangenerated Pseudorandom numbers are generated using algorithms that can text, exhibits human-like cognitive biases when generating produce long sequences of apparently random results, which are random number sequences. Initial findings indicate that entirely determined by an initial value known as a seed. While these ChatGPT-3.5 more effectively avoids repetitive and sequential patterns pseudorandom numbers appear unpredictable and successfully pass compared to humans, with notably lower repeat frequencies many statistical tests for randomness, they are not genuinely random and adjacent number frequencies. Continued research into different because their generation is algorithmically determined and models, parameters, and prompting methodologies will deepen our can theoretically be reproduced if the seed value is known [11, 25].

Due to rapid advancements in the development of Large Language Models (LLMs), programming these models with prompts has recently gained significant attention. However, the sheer number of available prompt engineering techniques creates an overwhelming landscape for practitioners looking to utilize these tools. For the most efficient and effective use of LLMs, it is important to compile a comprehensive list of prompting techniques and establish a standardized, interdisciplinary categorization framework. In this survey, we examine some of the most well-known prompting techniques from both academic and practical viewpoints and classify them into seven distinct categories. We present an overview of each category, aiming to clarify their unique contributions and showcase their practical applications in real-world examples in order to equip fellow practitioners with a structured framework for understanding and categorizing prompting techniques tailored to their specific domains. We believe that this approach will help simplify the complex landscape of prompt engineering and enable more effective utilization of LLMs in various applications. By providing practitioners with a systematic approach to prompt categorization, we aim to assist in navigating the intricacies of effective prompt design for conversational pre-trained LLMs and inspire new possibilities in their respective fields.

Fox News chief national security correspondent Jennifer Griffin has the latest on the retaliation against the drone attack on U.S. servicemembers in Jordan on'Your World.' The White House stressed Friday evening that the United States is "not looking for a war with Iran," saying the retaliatory strikes carried out in Syria and Iraq were designed to "de-escalate" tensions and "put an end" to attacks on U.S. troops in the region. The United States began retaliatory strikes on more than 85 targets in Iraq and Syria against Iran's Islamic Revolutionary Guards Corps (IRGC) Quds Force and affiliated militia groups and proxies. The strikes come in response to the deaths of three U.S. service members last Sunday on a U.S. base in Jordan. White House National Security Council Coordinator for Strategic Communications John Kirby stressed that the United States is not seeking conflict with Iran or in the Middle East, but explained that the strikes that began Friday evening "will not end" tonight.

Fox News White House correspondent Jacqui Heinrich has the latest on the pressure on Biden to respond to the attack that killed three U.S. service members, on Special Report. President Joe Biden and First Lady Jill Biden will on Friday take part in the dignified transfer of the remains of three troops killed in the Iran-backed militia attack in Jordan last weekend. The Bidens will join the grieving families of the three American service members who died when a drone struck a base, known as Tower 22, near the demilitarized zone on the border between Jordan and Syria. The Iraqi border is only six miles away. The fallen troops were Sgt.

Data preparation, also called data wrangling, is considered one of the most expensive and time-consuming steps when performing analytics or building machine learning models. Preparing data typically involves collecting and merging data from complex heterogeneous, and often large-scale data sources, such as data lakes. In this paper, we introduce a novel approach toward automatic data wrangling in an attempt to alleviate the effort of end-users, e.g. data analysts, in structuring dynamic views from data lakes in the form of tabular data. We aim to address table augmentation tasks, including row/column population and data imputation. Given a corpus of tables, we propose a retrieval augmented self-trained transformer model. Our self-learning strategy consists in randomly ablating tables from the corpus and training the retrieval-based model to reconstruct the original values or headers given the partial tables as input. We adopt this strategy to first train the dense neural retrieval model encoding table-parts to vectors, and then the end-to-end model trained to perform table augmentation tasks. We test on EntiTables, the standard benchmark for table augmentation, as well as introduce a new benchmark to advance further research: WebTables. Our model consistently and substantially outperforms both supervised statistical methods and the current state-of-the-art transformer-based models.

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