A group of organizations, including nonprofits like LatinoProsperity and labor groups like the California Teamsters, are petitioning California Attorney General Rob Bonta to stop OpenAI from becoming a for-profit entity, The Los Angeles Times reports. OpenAI announced plans to transition to a public-benefit corporation in 2024, and reportedly has two years to pull it off or risk a large portion of the money its raised become debt. The group's primary concerns are that OpenAI "failed to protect its charitable assets" and is actively "subverting its charitable mission to advance safe artificial intelligence." OpenAI started as a nonprofit research organization studying AI, but transitioned to a for-profit company that's overseen and run by a nonprofit in 2019. That structure is legally allowed in the state of California, but the group's petition claims that OpenAI's decision to pursue a new structure is driven by a desire not to further its mission, but to provide "AI's benefits -- the potential for untold profits and control over what may become powerful world-altering technologies -- to a handful of corporate investors and high-level employees."

Finding love in Washington state could come with a price. A bill proposed by two state Democratic lawmakers would impose a tax on dating apps. Under the terms of House Bill 2071, dating app companies would be required to pay 1 per Washington-based user each month, regardless of whether the user pays for the service. The money would be used to fund domestic violence programs. The money would be put into the newly created state Domestic Violence Services Account, which funds intervention programs and support services for victims.

A panel of New York judges condemned Jerome Dewald's use of an artificial intelligence-generated avatar as his attorney during an appearance in court on March 26. An artificial intelligence-generated avatar was the source of contempt inside a New York courtroom after judges quickly realized the attorney arguing a case in front of them was not real. The scene unfolded as Jerome Dewald, a plaintiff in an employment dispute, approached the stand of the New York State Supreme Court Appellate Division's First Judicial Department on March 26. "The appellant has submitted a video for his argument," Justice Sallie Manzanet-Daniels said. "We will hear that video now."

The rise of Large Language Models (LLMs) has brought about concerns regarding copyright infringement and unethical practices in data and model usage. For instance, slight modifications to existing LLMs may be used to falsely claim the development of new models, leading to issues of model copying and violations of ownership rights. This paper addresses these challenges by introducing a novel metric for quantifying LLM similarity, which leverages perplexity curves and differences in Menger curvature. Comprehensive experiments validate the performance of our methodology, demonstrating its superiority over baseline methods and its ability to generalize across diverse models and domains. Furthermore, we highlight the capability of our approach in detecting model replication through simulations, emphasizing its potential to preserve the originality and integrity of LLMs. Code is available at https://github.com/zyttt-coder/LLM_similarity.

AI development is shaped by academics and industry leaders - let us call them ``influencers'' - but it is unclear how their views align with those of the public. To address this gap, we developed an interactive platform that served as a data collection tool for exploring public views on AI, including their fears, hopes, and overall sense of hopefulness. We made the platform available to 330 participants representative of the U.S. population in terms of age, sex, ethnicity, and political leaning, and compared their views with those of 100 AI influencers identified by Time magazine. The public fears AI getting out of control, while influencers emphasize regulation, seemingly to deflect attention from their alleged focus on monetizing AI's potential. Interestingly, the views of AI influencers from underrepresented groups such as women and people of color often differ from the views of underrepresented groups in the public.

Compliance checking is the process of determining whether a regulated entity adheres to these regulations. Currently, compliance checking is predominantly manual, requiring significant time and highly skilled experts, while still being prone to errors caused by the human factor. Various approaches have been explored to automate compliance checking, however, representing regulations in OWL DL language which enables compliance checking through OWL reasoning has not been adopted. In this work, we propose an annotation schema and an algorithm that transforms text annotations into machine-interpretable OWL DL code. The proposed approach is validated through a proof-of-concept implementation applied to examples from the building construction domain.

To Give or Not to Give? The Impacts of Strategically Withheld Recourse Yatong Chen Andrew Estornell MPI for Intelligent Systems, T ubingen AI Center, T ubingen, Germany Bytedance Research Yevgeniy Vorobeychik Yang Liu Washington University in Saint Louis University of California, Santa Cruz Abstract Individuals often aim to reverse undesired outcomes in interactions with automated systems, like loan denials, by either implementing system-recommended actions (recourse), or manipulating their features. While providing recourse benefits users and enhances system utility, it also provides information about the decision process that can be used for more effective strategic manipulation, especially when the individuals collectively share such information with each other. We show that this tension leads rational utility-maximizing systems to frequently withhold recourse, resulting in decreased population utility, particularly impacting sensitive groups. To mitigate these effects, we explore ...

DBOT can value any public traded company on the basis of Damodaran's analysis, and generates a report to support its position in an attempt to mimic its analytic parent. Until recently, such capabilities of analytic twins for financial valuation were not feasible. However, with advances in large language models (LLMs) and generative artificial intelligence (GenAI), it has become possible to conduct valuations that marry numbers and reasoning to generate credible valuations that can be used for long-term investing. The implications for automation and support of various parts of the valuation exercise are profound. In this paper, we provide a method for creating a digital analytic twin, DBOT, which is designed to mimic the investment analysis of individual companies by Damodaran. Since DBOT can value every company in an index such as the S&P500, it also provide an analysis in a macro sense, for example, by valuing the S&P500 market index relative to the valuation of its individual components. From the perspective of generative AI, DBOT presents a multitude of challenges. First and foremost, LLMs must be able to reason over financial texts, charts, tables, and spreadsheets. Furthermore, DBOT requires the AI system to follow Damodaran's

Despite recent advancements in automatic summarization, state-of-the-art models do not summarize all documents equally well, raising the question: why? While prior research has extensively analyzed summarization models, little attention has been given to the role of document characteristics in influencing summarization performance. In this work, we explore two key research questions. First, do documents exhibit consistent summarization quality across multiple systems? If so, can we predict a document's summarization performance without generating a summary? We answer both questions affirmatively and introduce PreSumm, a novel task in which a system predicts summarization performance based solely on the source document. Our analysis sheds light on common properties of documents with low PreSumm scores, revealing that they often suffer from coherence issues, complex content, or a lack of a clear main theme. In addition, we demonstrate PreSumm's practical utility in two key applications: improving hybrid summarization workflows by identifying documents that require manual summarization and enhancing dataset quality by filtering outliers and noisy documents. Overall, our findings highlight the critical role of document properties in summarization performance and offer insights into the limitations of current systems that could serve as the basis for future improvements.

The use of AI in legal analysis and prediction (LegalAI) has gained widespread attention, with past research focusing on retrieval-based methods and fine-tuning large models. However, these approaches often require large datasets and underutilize the capabilities of modern large language models (LLMs). In this paper, inspired by the debate phase of real courtroom trials, we propose a novel legal judgment prediction model based on the Debate-Feedback architecture, which integrates LLM multi-agent debate and reliability evaluation models. Unlike traditional methods, our model achieves significant improvements in efficiency by minimizing the need for large historical datasets, thus offering a lightweight yet robust solution. Comparative experiments show that it outperforms several general-purpose and domain-specific legal models, offering a dynamic reasoning process and a promising direction for future LegalAI research.