The advent and rapid evolution of Large Language Models (LLMs) represent a profound shift in the artificial intelligence landscape [1, 2]. These models, distinguished by their significant learning capabilities, have demonstrated exceptional aptitude in generating coherent and contextually relevant text[3]. This prowess has rendered them invaluable across diverse sectors, including finance, healthcare, and autonomous systems, revolutionizing conventional approaches to tasks in these domains [4-7]. Nevertheless, the advent of LLMs into various societal aspects has also heightened the scrutiny of their reliability, especially the integrity of their generated content [8]. Amidst their impressive feats, LLMs' consistency in delivering accurate and verifiable information remains a pertinent concern [9, 10]. Instances of models producing misleading information or showcasing unwarranted confidence in incorrect outputs have underscored the imperative for ensuring the veracity of LLM outputs, notably in critical sectors where precision and factual accuracy are non-negotiable [11]. The phenomenon of "hallucination," wherein models fabricate information, has catalyzed the urgency to amplify the truthfulness of LLMs, positioning it as a pivotal research focus with substantial implications on future model refinement and application [12].

An important aim of this paper is to convey some basics of mathematical logic to the legal community working with Artificial Intelligence. After analysing what AI is, we decide to delimit ourselves to rule-based AI leaving Neural Networks and Machine Learning aside. Rule based AI allows for Formal methods which are described in a rudimentary form. We will then see how mathematical logic interacts with legal rule-based AI practice. We shall see how mathematical logic imposes limitations and complications to AI applications. We classify the limitations and interactions between mathematical logic and legal AI in three categories: logical, computational and mathematical. The examples to showcase the interactions will largely come from European traffic regulations. The paper closes off with some reflections on how and where AI could be used and on basic mechanisms that shape society.

A dataset is confounded if it is most easily solved via a spurious correlation which fails to generalize to new data. We will show that, in a continual learning setting where confounders may vary in time across tasks, the resulting challenge far exceeds the standard forgetting problem normally considered. In particular, we derive mathematically the effect of such confounders on the space of valid joint solutions to sets of confounded tasks. Interestingly, our theory predicts that for many such continual datasets, spurious correlations are easily ignored when the tasks are trained on jointly, but it is far harder to avoid confounding when they are considered sequentially. We construct such a dataset and demonstrate empirically that standard continual learning methods fail to ignore confounders, while training jointly on all tasks is successful. Our continually confounded dataset, ConCon, is based on CLEVR images and demonstrates the need for continual learning methods with more robust behavior with respect to confounding.

In academia, a growing body of research investigates the role of human values in designing ethical AI [12, 31, 74, 90]. Indeed, one of our leading AI research luminaries, Stuart Russell, believes the overarching goal of AI should change from "intelligence" to "intelligence provably aligned with human values" [74]. This call to arms gave birth to the value alignment problem. This challenge of engineering values into AI in response to the value alignment problem has resulted in a range of research areas: how human values can be learnt [43, 44, 45, 91]; how individual values can be aggregated to the level of groups [41]; how arguments that explicitly reference values can be made [7]; how decision making can be value-driven [14, 17, 21]; how online institutions can ensure value-aligned behaviours in hybrid communities [56, 57]; and how norms are selected or synthesised to maximise value-alignment [55, 80, 83]. Yet despite these efforts, no formal model of values exists today that provides a concrete foundational platform from which data structures and algorithms can be designed to build AI architectures that address the valuealignment problem. In response, we propose such a model built on the following guiding principles: 1) we employ a formal language to be precise about modelling values and related concepts [23, 47]; 2) we construct the formal components of this model to provide the foundations for the data structures and algorithmic design that will enable value-based reasoning; 3) we design the model to be agnostic on any specific implementation of values, though we do provide example implementation scenarios to illustrate the model's ubiquity and practical applicability; 4) we set out the model to subsume and relate to established concepts in AI research as much as possible; 5) we provide illustrative examples of building data structures and algorithms enabling value-based reasoning taken from our ongoing research applied to real-world use cases; 6) we ensure the model draws upon the wealth of work from within social psychology and explicitly demonstrate the grounding of our model within this research; and

Everyday we increasingly rely on machine learning models to automate and support high-stake tasks and decisions. This growing presence means that humans are now constantly interacting with machine learning-based systems, training and using models everyday. Several different techniques in computer science literature account for the human interaction with machine learning systems, but their classification is sparse and the goals varied. This survey proposes a taxonomy of Hybrid Decision Making Systems, providing both a conceptual and technical framework for understanding how current computer science literature models interaction between humans and machines.

Interactions between the government officials and civilians affect public wellbeing and the state legitimacy that is necessary for the functioning of democratic society. Police officers, the most visible and contacted agents of the state, interact with the public more than 20 million times a year during traffic stops. Today, these interactions are regularly recorded by body-worn cameras (BWCs), which are lauded as a means to enhance police accountability and improve police-public interactions. However, the timely analysis of these recordings is hampered by a lack of reliable automated tools that can enable the analysis of these complex and contested police-public interactions. This article proposes an approach to developing new multi-perspective, multimodal machine learning (ML) tools to analyze the audio, video, and transcript information from this BWC footage. Our approach begins by identifying the aspects of communication most salient to different stakeholders, including both community members and police officers. We move away from modeling approaches built around the existence of a single ground truth and instead utilize new advances in soft labeling to incorporate variation in how different observers perceive the same interactions. We argue that this inclusive approach to the conceptualization and design of new ML tools is broadly applicable to the study of communication and development of analytic tools across domains of human interaction, including education, medicine, and the workplace.

FOX News' Eben Brown reports that with the use of AI, scammers are fleecing Americans in more sophisticated ways. The Federal Communications Commission on Thursday made AI-generated robocalls mimicking the voices of political candidates to fool voters illegal. With the unanimous adoption of a declaratory ruling that recognizes calls made with AI-generated voices are "artificial" under the Telephone Consumer Protection Act (TCPA), a 1991 law restricting junk calls that use artificial and prerecorded voice messages, the FCC said it was giving state attorneys general new tools to go after those responsible for voice cloning scams. The decision was announced days after New Hampshire Attorney General John Formella revealed earlier this week that nefarious robocalls with an AI-generated clone of President Biden's voice urging recipients not to participate in the Jan. 23 primaries – and instead save their votes for the November election – had been traced to two Texas companies. Formella vowed potential civil and criminal action at the state and federal level.

Scam and spam robocalls featuring lifelike AI-generated human voices are now officially illegal, in a unanimous ruling by the Federal Communications Commission. The new ruling, issued Thursday, promised to give'State Attorneys General across the country new tools to go after bad actors behind these nefarious robocalls.' 'Bad actors are using AI-generated voices in unsolicited robocalls to extort vulnerable family members, imitate celebrities, and misinform voters,' FCC Chairwoman Jessica Rosenworcel said in a press release. Following the new ruling, FCC Chairwoman Jessica Rosenworcel (above) said, 'We're putting the fraudsters behind these robocalls on notice.' 'State Attorneys General will now have new tools to crack down on these scams and ensure the public is protected from fraud and misinformation,' Rosenworcel said. The FCC ruling will expand what activities prosecutors can pursue under the Telephone Consumer Protection Act (TCPA), which is currently the primary law allowing the authorities to help limit junk calls.

The US government on Thursday outlawed robocalls that use voices generated by artificial intelligence, a decision that sends a clear message that exploiting the technology to scam people and mislead voters won't be tolerated. The unanimous ruling by the Federal Communications Commission (FCC) targets robocalls made with AI voice-cloning tools under the Telephone Consumer Protection Act, a 1991 law restricting junk calls that use artificial and prerecorded voice messages. The announcement comes as New Hampshire authorities are advancing their investigation into AI-generated robocalls that mimicked President Joe Biden's voice to discourage people from voting in the state's first-in-the-nation primary last month. Effective immediately, the regulation empowers the FCC to fine companies that use AI voices in their calls or block the service providers that carry them. It also opens the door for call recipients to file lawsuits and gives state attorneys general a new mechanism to crack down on violators, according to the FCC.

It's now illegal in the US for robocallers to use AI-generated voices, thanks to a new ruling by the Federal Communications Commission on Thursday. In a unanimous decision, the FCC expands the Telephone Consumer Protection Act, or TCPA, to cover robocall scams that contain AI voice clones. The new rule goes into effect immediately, allowing the commission to fine companies and block providers for making these types of calls. "Bad actors are using AI-generated voices in unsolicited robocalls to extort vulnerable family members, imitate celebrities, and misinform voters," FCC chair Jessica Rosenworcel said in a statement on Thursday. The move comes a few days after the FCC and New Hampshire attorney general John Formella identified Life Corporation as the company behind the mysterious robocalls imitating President Joe Biden last month before the state's primary election.