This paper explores the integration of human rights considerations into AI regulatory frameworks across different IEEE regions - specifically the United States (Region 1-6), Europe (Region 8), China (part of Region 10), and Singapore (part of Region 10). While all acknowledge the transformative potential of AI and the necessity of ethical guidelines, their regulatory approaches significantly differ. Europe exhibits a rigorous framework with stringent protections for individual rights, while the U.S. promotes innovation with less restrictive regulations. China emphasizes state control and societal order in its AI strategies. In contrast, Singapore's advisory framework encourages self-regulation and aligns closely with international norms. This comparative analysis underlines the need for ongoing global dialogue to harmonize AI regulations that safeguard human rights while promoting technological advancement, reflecting the diverse perspectives and priorities of each region.

Large Language Models (LLMs) are advancing at an amazing speed and have become indispensable across academia, industry, and daily applications. To keep pace with the status quo, this survey probes the core challenges that the rise of LLMs poses for evaluation. We identify and analyze two pivotal transitions: (i) from task-specific to capability-based evaluation, which reorganizes benchmarks around core competencies such as knowledge, reasoning, instruction following, multi-modal understanding, and safety; and (ii) from manual to automated evaluation, encompassing dynamic dataset curation and "LLM-as-a-judge" scoring. Yet, even with these transitions, a crucial obstacle persists: the evaluation generalization issue. Bounded test sets cannot scale alongside models whose abilities grow seemingly without limit. We will dissect this issue, along with the core challenges of the above two transitions, from the perspectives of methods, datasets, evaluators, and metrics. Due to the fast evolving of this field, we will maintain a living GitHub repository (links are in each section) to crowd-source updates and corrections, and warmly invite contributors and collaborators.

As the US tax law evolves to adapt to ever-changing politico-economic realities, tax preparation software plays a significant role in helping taxpayers navigate these complexities. The dynamic nature of tax regulations poses a significant challenge to accurately and timely maintaining tax software artifacts. The state-of-the-art in maintaining tax prep software is time-consuming and error-prone as it involves manual code analysis combined with an expert interpretation of tax law amendments. We posit that the rigor and formality of tax amendment language, as expressed in IRS publications, makes it amenable to automatic translation to executable specifications (code). Our research efforts focus on identifying, understanding, and tackling technical challenges in leveraging Large Language Models (LLMs), such as ChatGPT and Llama, to faithfully extract code differentials from IRS publications and automatically integrate them with the prior version of the code to automate tax prep software maintenance.

The Banking, Financial Services, and Insurance (BFSI) sector operates on vast volumes of highly sensitive customer data, creating an enduring tension between the drive for data-driven insights and the imperative to comply with strict privacy and security regulations such as GDPR [1] and CCP A [2]. Traditional anonymization methods like masking, aggregation, k-anonymity, L-diversity, and T-closeness often degrade data quality to the point where sophisticated analytics, fraud detection, risk modeling, and machine learning applications suffer significant performance drops. Moreover, these legacy approaches can remain vulnerable to linkage and inference attacks, undermining both privacy guarantees and competitive innovation in financial institutions. The need for advanced techniques that can create privacy-preserving datasets without sacrificing analytical utility is paramount. In response, advanced techniques for creating privacy-preserving datasets have emerged, broadly categorized as purely synthetic data generation and advanced data perturbation. Purely synthetic data, often created using deep generative models (like GANs), aims to capture the statistical patterns of real data without any one-to-one mapping to real individuals. Advanced data perturbation applies carefully calibrated noise, transformations, and privacy-enhancing techniques like differential privacy to original datasets, seeking to obscure sensitive information while retaining analytical value. These methods can include context-aware transformations, where the nature of the data and its intended use inform the perturbation strategy, ensuring that the resulting dataset remains useful for specific tasks. However, the challenge remains to balance privacy and utility effectively. Traditional methods often fail to provide sufficient privacy guarantees or result in datasets that are too noisy for practical use.

The de-identification of private information in medical data is a crucial process to mitigate the risk of confidentiality breaches, particularly when patient personal details are not adequately removed before the release of medical records. Although rule-based and learning-based methods have been proposed, they often struggle with limited generalizability and require substantial amounts of annotated data for effective performance. Recent advancements in large language models (LLMs) have shown significant promise in addressing these issues due to their superior language comprehension capabilities. However, LLMs present challenges, including potential privacy risks when using commercial LLM APIs and high computational costs for deploying open-source LLMs locally. In this work, we introduce LPPA, an LLM-empowered Privacy-Protected PHI Annotation framework for clinical notes, targeting the English language. By fine-tuning LLMs locally with synthetic notes, LPPA ensures strong privacy protection and high PHI annotation accuracy. Extensive experiments demonstrate LPPA's effectiveness in accurately de-identifying private information, offering a scalable and efficient solution for enhancing patient privacy protection.

Large Language Models (LLMs) have exhibited impressive natural language processing capabilities but often perpetuate social biases inherent in their training data. To address this, we introduce MultiLingual Augmented Bias Testing (MLA-BiTe), a framework that improves prior bias evaluation methods by enabling systematic multilingual bias testing. MLA-BiTe leverages automated translation and paraphrasing techniques to support comprehensive assessments across diverse linguistic settings. In this study, we evaluate the effectiveness of MLA-BiTe by testing four state-of-the-art LLMs in six languages -- including two low-resource languages -- focusing on seven sensitive categories of discrimination.

Elon Musk and his companies face at least 2.37bn in legal exposure from federal investigations, litigation and regulatory oversight, according to a new report from Senate Democrats. The report attempts to put a number to Musk's many conflicts of interest through his work with his so-called "department of government efficiency" (Doge), warning that he may seek to use his influence to avoid legal liability. The report, which was published on Monday by Democratic members of the Senate homeland security committee's permanent subcommittee on investigations, looked at 65 actual or potential actions against Musk across 11 separate agencies. Investigators calculated the financial liabilities Musk and his companies, such as Tesla, SpaceX and Neuralink, may face in 45 of those actions. Since Donald Trump won re-election last year and Musk took on the role of de facto head of Doge in January, ethics watchdogs and Democratic officials have warned that the Tesla CEO could use his power to oust regulators and quash investigations into his companies.

Artificial intelligence "nudification" apps that create deepfake sexual images of children should be immediately banned, amid growing fears among teenage girls that they could fall victim, the children's commissioner for England is warning. Girls said they were stopping posting images of themselves on social media out of a fear that generative AI tools could be used to digitally remove their clothes or sexualise them, according to the commissioner's report on the tools, drawing on children's experiences. Although it is illegal to create or share a sexually explicit image of a child, the technology enabling them remains legal, the report noted. "Children have told me they are frightened by the very idea of this technology even being available, let alone used. They fear that anyone – a stranger, a classmate, or even a friend – could use a smartphone as a way of manipulating them by creating a naked image using these bespoke apps," the commissioner, Dame Rachel de Souza, said.

Generating images from prompts containing specific entities requires models to retain as much entity-specific knowledge as possible. However, fully memorizing such knowledge is impractical due to the vast number of entities and their continuous emergence. To address this, we propose Text-based Intelligent Generation with Entity prompt Refinement (TextTIGER), which augments knowledge on entities included in the prompts and then summarizes the augmented descriptions using Large Language Models (LLMs) to mitigate performance degradation from longer inputs. To evaluate our method, we introduce WiT-Cub (WiT with Captions and Uncomplicated Background-explanations), a dataset comprising captions, images, and an entity list. Experiments on four image generation models and five LLMs show that TextTIGER improves image generation performance in standard metrics (IS, FID, and CLIPScore) compared to caption-only prompts. Additionally, multiple annotators' evaluation confirms that the summarized descriptions are more informative, validating LLMs' ability to generate concise yet rich descriptions. These findings demonstrate that refining prompts with augmented and summarized entity-related descriptions enhances image generation capabilities. The code and dataset will be available upon acceptance.

Given the high computational complexity of decision tree estimation, classical methods construct a tree by adding one node at a time in a recursive way. To facilitate promoting fairness, we propose a fairness criterion local to the tree nodes. We prove how it is related to the Statistical Parity criterion, popular in the Algorithmic Fairness literature, and show how to incorporate it into standard recursive tree estimation algorithms. We present a tree estimation algorithm called Constrained Logistic Regression Tree (C-LRT), which is a modification of the standard CART algorithm using locally linear classifiers and imposing restrictions as done in Constrained Logistic Regression. Finally, we evaluate the performance of trees estimated with C-LRT on datasets commonly used in the Algorithmic Fairness literature, using various classification and fairness metrics. The results confirm that C-LRT successfully allows to control and balance accuracy and fairness.