While companies like Microsoft and Nvidia are all-in on the power of next-generation machine learning algorithms, some regulators are dreading what it might mean for our already-stressed communication networks. The chairwoman of the US Federal Communications Commission, for one, who's just proposed an investigation into what "AI" could mean for even more spam calls and texts. The FCC will vote to adopt a multi-tiered action in November. Chairwoman Rosencworcel, who's served on the Commission since 2012 and as its executive since being confirmed late in 2021, is particularly concerned with how newly empowered AI tools could affect senior citizens. The FCC's initial press release (PDF link) lists four main goals: determining whether AI technologies fall under the Comission's jurisdiction via the Telephone Consumer Protection Act of 1991, if and when future AI tech might do the same, how AI impacts existing regulatory frameworks, and if the FCC should consider ways to verify the authenticity of auto-generated AI voice and text from "trusted sources."

The EU is within "touching distance" of passing the world's first laws on artificial intelligence, giving Brussels the power to shut down services that cause harm to society, says the AI tsar who has spent the last four years developing the legislation. "Artificial intelligence does have a profound impact on everything we do and therefore it was time to bring in some safeguards and guardrails on how this technology will evolve for the benefit of our citizens" said Dragoș Tudorache, a Romanian MEP and co-rapporteur of the parliamentary committee steering through the legislation, in an exclusive interview with the Guardian. Speaking in his Brussels parliamentary office, Tudorache said: "I'm more optimistic than I am pessimistic about AI. I would be a pessimist if we did nothing about it." Tudorache said there was a chance he could get a final text agreed for the AI Act by Wednesday.

Deepfakes, viral online challenges and protecting freedom of expression: the online safety bill sprawls across many corners of the internet and it's about to become official. The much-debated legislation is due to receive royal assent, and therefore become law, imminently. The purpose of the act is to make sure tech firms have the right moderating systems and processes in place to deal with harmful material. "This means a company cannot comply by chance," says Ben Packer, a partner at the law firm Linklaters. "It must have systems and processes in place to, for instance, minimise the length of time for which illegal content is present." Packer adds that if those systems and processes are deemed to be not up to scratch, platforms could be in breach of the act.

Aligning AI agents to human intentions and values is a key bottleneck in building safe and deployable AI applications. But whose values should AI agents be aligned with? Reinforcement learning with human feedback (RLHF) has emerged as the key framework for AI alignment. RLHF uses feedback from human reinforcers to fine-tune outputs; all widely deployed large language models (LLMs) use RLHF to align their outputs to human values. It is critical to understand the limitations of RLHF and consider policy challenges arising from these limitations. In this paper, we investigate a specific challenge in building RLHF systems that respect democratic norms. Building on impossibility results in social choice theory, we show that, under fairly broad assumptions, there is no unique voting protocol to universally align AI systems using RLHF through democratic processes. Further, we show that aligning AI agents with the values of all individuals will always violate certain private ethical preferences of an individual user i.e., universal AI alignment using RLHF is impossible. We discuss policy implications for the governance of AI systems built using RLHF: first, the need for mandating transparent voting rules to hold model builders accountable. Second, the need for model builders to focus on developing AI agents that are narrowly aligned to specific user groups.

In maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transshipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) message transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models. Using historical data, the trained model predicts if a message should be received in the upcoming minute or not. Afterwards, the model reports on detected anomalies by comparing the prediction with what actually happens. Our method can process AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. Using related research results, we validated our method by rediscovering already detected intentional AIS shutdowns.

The jailbreak attack can bypass the safety measures of a Large Language Model (LLM), generating harmful content. This misuse of LLM has led to negative societal consequences. Currently, there are two main approaches to address jailbreak attacks: safety training and safeguards. Safety training focuses on further training LLM to enhance its safety. On the other hand, safeguards involve implementing external models or filters to prevent harmful outputs. However, safety training has constraints in its ability to adapt to new attack types and often leads to a drop in model performance. Safeguards have proven to be of limited help. To tackle these issues, we propose a novel approach called Self-Guard, which combines the strengths of both safety methods. Self-Guard includes two stages. In the first stage, we enhance the model's ability to assess harmful content, and in the second stage, we instruct the model to consistently perform harmful content detection on its own responses. The experiment has demonstrated that Self-Guard is robust against jailbreak attacks. In the bad case analysis, we find that LLM occasionally provides harmless responses to harmful queries. Additionally, we evaluated the general capabilities of the LLM before and after safety training, providing evidence that Self-Guard does not result in the LLM's performance degradation. In sensitivity tests, Self-Guard not only avoids inducing over-sensitivity in LLM but also can even mitigate this issue.

We present DALE, a novel and effective generative Data Augmentation framework for low-resource LEgal NLP. DALE addresses the challenges existing frameworks pose in generating effective data augmentations of legal documents - legal language, with its specialized vocabulary and complex semantics, morphology, and syntax, does not benefit from data augmentations that merely rephrase the source sentence. To address this, DALE, built on an Encoder-Decoder Language Model, is pre-trained on a novel unsupervised text denoising objective based on selective masking - our masking strategy exploits the domain-specific language characteristics of templatized legal documents to mask collocated spans of text. Denoising these spans helps DALE acquire knowledge about legal concepts, principles, and language usage. Consequently, it develops the ability to generate coherent and diverse augmentations with novel contexts. Finally, DALE performs conditional generation to generate synthetic augmentations for low-resource Legal NLP tasks. We demonstrate the effectiveness of DALE on 13 datasets spanning 6 tasks and 4 low-resource settings. DALE outperforms all our baselines, including LLMs, qualitatively and quantitatively, with improvements of 1%-50%.

Patents are legal documents that aim at protecting inventions on the one hand and at making technical knowledge circulate on the other. Their complex style -- a mix of legal, technical, and extremely vague language -- makes their content hard to access for humans and machines and poses substantial challenges to the information retrieval community. This paper proposes an approach to automatically simplify patent text through rephrasing. Since no in-domain parallel simplification data exist, we propose a method to automatically generate a large-scale silver standard for patent sentences. To obtain candidates, we use a general-domain paraphrasing system; however, the process is error-prone and difficult to control. Thus, we pair it with proper filters and construct a cleaner corpus that can successfully be used to train a simplification system. Human evaluation of the synthetic silver corpus shows that it is considered grammatical, adequate, and contains simple sentences.

Similar case retrieval (SCR) is a representative legal AI application that plays a pivotal role in promoting judicial fairness. However, existing SCR datasets only focus on the fact description section when judging the similarity between cases, ignoring other valuable sections (e.g., the court's opinion) that can provide insightful reasoning process behind. Furthermore, the case similarities are typically measured solely by the textual semantics of the fact descriptions, which may fail to capture the full complexity of legal cases from the perspective of legal knowledge. In this work, we present MUSER, a similar case retrieval dataset based on multi-view similarity measurement and comprehensive legal element with sentence-level legal element annotations. Specifically, we select three perspectives (legal fact, dispute focus, and law statutory) and build a comprehensive and structured label schema of legal elements for each of them, to enable accurate and knowledgeable evaluation of case similarities. The constructed dataset originates from Chinese civil cases and contains 100 query cases and 4,024 candidate cases. We implement several text classification algorithms for legal element prediction and various retrieval methods for retrieving similar cases on MUSER. The experimental results indicate that incorporating legal elements can benefit the performance of SCR models, but further efforts are still required to address the remaining challenges posed by MUSER. The source code and dataset are released at https://github.com/THUlawtech/MUSER.

The powerful ability to understand, follow, and generate complex language emerging from large language models (LLMs) makes LLM-generated text flood many areas of our daily lives at an incredible speed and is widely accepted by humans. As LLMs continue to expand, there is an imperative need to develop detectors that can detect LLM-generated text. This is crucial to mitigate potential misuse of LLMs and safeguard realms like artistic expression and social networks from harmful influence of LLM-generated content. The LLM-generated text detection aims to discern if a piece of text was produced by an LLM, which is essentially a binary classification task. The detector techniques have witnessed notable advancements recently, propelled by innovations in watermarking techniques, zero-shot methods, fine-turning LMs methods, adversarial learning methods, LLMs as detectors, and human-assisted methods. In this survey, we collate recent research breakthroughs in this area and underscore the pressing need to bolster detector research. We also delve into prevalent datasets, elucidating their limitations and developmental requirements. Furthermore, we analyze various LLM-generated text detection paradigms, shedding light on challenges like out-of-distribution problems, potential attacks, and data ambiguity. Conclusively, we highlight interesting directions for future research in LLM-generated text detection to advance the implementation of responsible artificial intelligence (AI). Our aim with this survey is to provide a clear and comprehensive introduction for newcomers while also offering seasoned researchers a valuable update in the field of LLM-generated text detection. The useful resources are publicly available at: https://github.com/NLP2CT/LLM-generated-Text-Detection.