Fair representation learning (FRL) is a popular class of methods aiming to produce fair classifiers via data preprocessing. Recent regulatory directives stress the need for FRL methods that provide practical certificates, i.e., provable upper bounds on the unfairness of any downstream classifier trained on preprocessed data, which directly provides assurance in a practical scenario. Creating such FRL methods is an important challenge that remains unsolved. In this work, we address that challenge and introduce FARE (Fairness with Restricted Encoders), the first FRL method with practical fairness certificates. FARE is based on our key insight that restricting the representation space of the encoder enables the derivation of practical guarantees, while still permitting favorable accuracy-fairness tradeoffs for suitable instantiations, such as one we propose based on fair trees. To produce a practical certificate, we develop and apply a statistical procedure that computes a finite sample high-confidence upper bound on the unfairness of any downstream classifier trained on FARE embeddings. In our comprehensive experimental evaluation, we demonstrate that FARE produces practical certificates that are tight and often even comparable with purely empirical results obtained by prior methods, which establishes the practical value of our approach.

Named Entity Recognition (NER) has seen significant progress in recent years, with numerous state-of-the-art (SOTA) models achieving high performance. However, very few studies have focused on the generation of entities' context. In this paper, we introduce CONTEXT-NER, a task that aims to generate the relevant context for entities in a sentence, where the context is a phrase describing the entity but not necessarily present in the sentence. To facilitate research in this task, we also present the EDGAR10-Q dataset, which consists of annual and quarterly reports from the top 1500 publicly traded companies. The dataset is the largest of its kind, containing 1M sentences, 2.8M entities, and an average of 35 tokens per sentence, making it a challenging dataset. We propose a baseline approach that combines a phrase generation algorithm with inferencing using a 220M language model, achieving a ROUGE-L score of 27% on the test split. Additionally, we perform a one-shot inference with ChatGPT, which obtains a 30% ROUGE-L, highlighting the difficulty of the dataset. We also evaluate models such as T5 and BART, which achieve a maximum ROUGE-L of 49% after supervised finetuning on EDGAR10-Q. We also find that T5-large, when pre-finetuned on EDGAR10-Q, achieve SOTA results on downstream finance tasks such as Headline, FPB, and FiQA SA, outperforming vanilla version by 10.81 points. To our surprise, this 66x smaller pre-finetuned model also surpasses the finance-specific LLM BloombergGPT-50B by 15 points. We hope that our dataset and generated artifacts will encourage further research in this direction, leading to the development of more sophisticated language models for financial text analysis

OpenAI CEO Sam Altman, the artificial intelligence lab behind ChatGPT, took questions from reporters following his congressional hearing, including defining "scary AI." Sen. Josh Hawley, R-Mo, unveiled a set of "guiding principles" ahead of any future artificial intelligence legislation Wednesday, seeking to "protect Americans' privacy" as the technology continues to develop. The Republican senator outlined five principles, first reported by Axios, aimed to "help set the course for the responsible development of American AI," as lawmakers figure out how to deal with current and future advancements. "Congress can and should act to protect Americans' privacy, stave off the harms of unchecked AI development, insulate kids from harmful impacts, and keep this valuable technology out of the hands of our adversaries," Hawley said in a statement. The recent leaps in easily-accessible AI technology like ChatGPT have led both lawmakers and industry leaders to recognize the need for regulation.

The latest digital security bulletin from the FBI is sure to turn some heads, in both the literal and figurative sense. According to the US federal law enforcement agency, criminals are using AI-generated images to put a new spin on blackmail. They've been seen using publicly-posted images on social media and running them through an AI image generator to create convincing (but entirely fake) nude photos, then extorting the victims for money or real photos, in a practice the bureau is calling "sextortion." This sort of thing isn't exactly new -- nothing was stopping malefactors from using social media selfies and Photoshop before, and that's happened in some isolated cases. The danger comes from the ease of access to this technique created by new AI "deepfake" image tools. Now criminals don't need months or years of experience in convincing image manipulation, they just need a few photos and the right software.

A former executive at TikTok's parent company, ByteDance, has alleged that the Chinese Communist party accessed user data from the social video app belonging to Hong Kong protesters and civil rights activists. Yintao Yu, a former head of engineering at ByteDance's US operation, claimed in a legal filing that a committee of Communist party members accessed TikTok data that included the users' network information, Sim card identifications and IP addresses in a bid to identify the individuals and their locations. The claims, in a wrongful dismissal lawsuit brought by Yu in a California court and reported by the Wall Street Journal, also allege the party accessed TikTok users' communications, monitored Hong Kong users who uploaded protest-related content and that Beijing-based ByteDance maintained a "backdoor channel" for the party to access US user data. Yu alleges in the filing that members of a Communist party committee inside ByteDance had access to a "superuser" credential which was also called a "God credential" and allowed them to view all data collected by ByteDance. The filing adds that when Yu was at ByteDance, between August 2017 and November 2018, TikTok stored all users' direct messages, search histories and content viewed by users.

Sen. Marsha Blackburn, R-Tenn., shares her takeaways from Tuesday's AI hearing with OpenAI CEO Sam Altman. She also reveals what next steps she and her colleagues are prepared to take to protect consumer data amid the AI boom. Sen. Marsha Blackburn, R-Tenn., is calling on Congress to pass an internet user privacy standard as a first step toward making sure Americans are knowledgeable and their data safe amid the rapid advancement of artificial intelligence (AI) technology. Blackburn is one of four Republicans on the Senate Judiciary subcommittee on intellectual property (IP). The panel is holding a hearing Wednesday afternoon titled, "Artificial Intelligence and Intellectual Property – Part I: Patents, Innovation, and Competition."

A new class of tools, colloquially called generative AI, can produce high-quality artistic media for visual arts, concept art, music, fiction, literature, video, and animation. The generative capabilities of these tools are likely to fundamentally alter the creative processes by which creators formulate ideas and put them into production. As creativity is reimagined, so too may be many sectors of society. Understanding the impact of generative AI - and making policy decisions around it - requires new interdisciplinary scientific inquiry into culture, economics, law, algorithms, and the interaction of technology and creativity. We argue that generative AI is not the harbinger of art's demise, but rather is a new medium with its own distinct affordances. In this vein, we consider the impacts of this new medium on creators across four themes: aesthetics and culture, legal questions of ownership and credit, the future of creative work, and impacts on the contemporary media ecosystem. Across these themes, we highlight key research questions and directions to inform policy and beneficial uses of the technology.

This paper presents the NOWJ team's approach to the COL-IEE 2023 Competition, which focuses on advancing legal information processing techniques and applying them to real-world legal scenarios. Our team tackles the four tasks in the competition, which involve legal case retrieval, legal case entailment, statute law retrieval, and legal textual entailment. We employ state-of-the-art machine learning models and innovative approaches, such as BERT, Longformer, BM25-ranking algorithm, and multi-task learning models. Although our team did not achieve state-of-the-art results, our findings provide valuable insights and pave the way for future improvements in legal information processing.

Question answering (QA) in law is a challenging problem because legal documents are much more complicated than normal texts in terms of terminology, structure, and temporal and logical relationships. It is even more difficult to perform legal QA for low-resource languages like Vietnamese where labeled data are rare and pre-trained language models are still limited. In this paper, we try to overcome these limitations by implementing a Vietnamese article-level retrieval-based legal QA system and introduce a novel method to improve the performance of language models by improving data quality through weak labeling. Our hypothesis is that in contexts where labeled data are limited, efficient data enrichment can help increase overall performance. Our experiments are designed to test multiple aspects, which demonstrate the effectiveness of the proposed technique.

Traditional text classification typically categorizes texts into pre-defined coarse-grained classes, from which the produced models cannot handle the real-world scenario where finer categories emerge periodically for accurate services. In this work, we investigate the setting where fine-grained classification is done only using the annotation of coarse-grained categories and the coarse-to-fine mapping. We propose a lightweight contrastive clustering-based bootstrapping method to iteratively refine the labels of passages. During clustering, it pulls away negative passage-prototype pairs under the guidance of the mapping from both global and local perspectives. Experiments on NYT and 20News show that our method outperforms the state-of-the-art methods by a large margin.