Autonomous vessels have emerged as a prominent and accepted solution, particularly in the naval defence sector. However, achieving full autonomy for marine vessels demands the development of robust and reliable control and guidance systems that can handle various encounters with manned and unmanned vessels while operating effectively under diverse weather and sea conditions. A significant challenge in this pursuit is ensuring the autonomous vessels' compliance with the International Regulations for Preventing Collisions at Sea (COLREGs). These regulations present a formidable hurdle for the human-level understanding by autonomous systems as they were originally designed from common navigation practices created since the mid-19th century. Their ambiguous language assumes experienced sailors' interpretation and execution, and therefore demands a high-level (cognitive) understanding of language and agent intentions. These capabilities surpass the current state-of-the-art in intelligent systems. This position paper highlights the critical requirements for a trustworthy control and guidance system, exploring the complexity of adapting COLREGs for safe vessel-on-vessel encounters considering autonomous maritime technology competing and/or cooperating with manned vessels.

To address the growing demand for privacy protection in machine learning, we propose a novel and efficient machine unlearning approach for \textbf{L}arge \textbf{M}odels, called \textbf{LM}Eraser. Existing unlearning research suffers from entangled training data and complex model architectures, incurring extremely high computational costs for large models. LMEraser takes a divide-and-conquer strategy with a prompt tuning architecture to isolate data influence. The training dataset is partitioned into public and private datasets. Public data are used to train the backbone of the model. Private data are adaptively clustered based on their diversity, and each cluster is used to optimize a prompt separately. This adaptive prompt tuning mechanism reduces unlearning costs and maintains model performance. Experiments demonstrate that LMEraser achieves a $100$-fold reduction in unlearning costs without compromising accuracy compared to prior work. Our code is available at: \url{https://github.com/lmeraser/lmeraser}.

Child sexual exploitation is on the rise online and taking new forms such as images and videos generated by artificial intelligence, according to an annual assessment released on Tuesday by the National Center for Missing & Exploited Children (NCMEC), a US-based clearinghouse for the reporting of child sexual abuse material. Reports to the NCMEC of child abuse online rose by more than 12% in 2023 compared with the previous year, surpassing 36.2m The majority of tips received were related to the circulation of child sexual abuse material (CSAM) such as photos and videos, but there was also an increase in reports of financial sexual extortion, when an online predator lures a child into sending nude images or videos and then demands money. Some children and families were extorted for financial gain by predators using AI-made CSAM, according to the NCMEC. The center received 4,700 reports of images or videos of the sexual exploitation of children made by generative AI, a category it only started tracking in 2023, a spokesperson said.

When police departments first started buying and deploying bodycams in the wake of the police killing of Michael Brown in Ferguson, Missouri, a decade ago, activists hoped it would bring about real change. Years later, despite what's become a multibillion-dollar market for these devices, the tech is far from a panacea. Most of the vast reams of footage they generate go unwatched. And if they do finally provide video to the public, it's often selectively edited, lacking context and failing to tell the complete story. A handful of AI startups see this problem as an opportunity to create what are essentially bodycam-to-text programs for different players in the legal system, mining this footage for misdeeds.

Meta's Oversight Board is once again taking on the social network's rules for AI-generated content. The board has accepted two cases that deal with AI-made explicit images of public figures. While Meta's rules already prohibit nudity on Facebook and Instagram, the board said in a statement that it wants to address whether "Meta's policies and its enforcement practices are effective at addressing explicit AI-generated imagery." Sometimes referred to as "deepfake porn," AI-generated images of female celebrities, politicians and other public figures has become an increasingly prominent form of online harassment and has drawn a wave of proposed regulation. With the two cases, the Oversight Board could push Meta to adopt new rules to address such harassment on its platform.

The U.K. will criminalize the creation of sexually explicit deepfake images as part of plans to tackle violence against women. People convicted of creating such deepfakes without consent, even if they don't intend to share the images, will face prosecution and an unlimited fine under a new law, the Ministry of Justice said in a statement. Sharing the images could also result in jail. Rapid developments in artificial intelligence have led to the rise of the creation and dissemination of deepfake images and videos. The U.K. has classified violence against women and girls as a national threat, which means the police must prioritize tackling it, and this law is designed to help them clamp down on a practice that is increasingly being used to humiliate or distress victims.

Predicting legal judgments with reliable confidence is paramount for responsible legal AI applications. While transformer-based deep neural networks (DNNs) like BERT have demonstrated promise in legal tasks, accurately assessing their prediction confidence remains crucial. We present a novel Bayesian approach called BayesJudge that harnesses the synergy between deep learning and deep Gaussian Processes to quantify uncertainty through Bayesian kernel Monte Carlo dropout. Our method leverages informative priors and flexible data modelling via kernels, surpassing existing methods in both predictive accuracy and confidence estimation as indicated through brier score. Extensive evaluations of public legal datasets showcase our model's superior performance across diverse tasks. We also introduce an optimal solution to automate the scrutiny of unreliable predictions, resulting in a significant increase in the accuracy of the model's predictions by up to 27\%. By empowering judges and legal professionals with more reliable information, our work paves the way for trustworthy and transparent legal AI applications that facilitate informed decisions grounded in both knowledge and quantified uncertainty.

With the development of legal intelligence, Criminal Court View Generation has attracted much attention as a crucial task of legal intelligence, which aims to generate concise and coherent texts that summarize case facts and provide explanations for verdicts. Existing researches explore the key information in case facts to yield the court views. Most of them employ a coarse-grained approach that partitions the facts into broad segments (e.g., verdict-related sentences) to make predictions. However, this approach fails to capture the complex details present in the case facts, such as various criminal elements and legal events. To this end, in this paper, we propose an Event Grounded Generation (EGG) method for criminal court view generation with cooperative (Large) Language Models, which introduces the fine-grained event information into the generation. Specifically, we first design a LLMs-based extraction method that can extract events in case facts without massive annotated events. Then, we incorporate the extracted events into court view generation by merging case facts and events. Besides, considering the computational burden posed by the use of LLMs in the extraction phase of EGG, we propose a LLMs-free EGG method that can eliminate the requirement for event extraction using LLMs in the inference phase. Extensive experimental results on a real-world dataset clearly validate the effectiveness of our proposed method.

As large language models (LLMs) become ubiquitous in our daily tasks and digital interactions, associated privacy risks are increasingly in focus. While LLM privacy research has primarily focused on the leakage of model training data, it has recently been shown that the increase in models' capabilities has enabled LLMs to make accurate privacy-infringing inferences from previously unseen texts. With the rise of multimodal vision-language models (VLMs), capable of understanding both images and text, a pertinent question is whether such results transfer to the previously unexplored domain of benign images posted online. To investigate the risks associated with the image reasoning capabilities of newly emerging VLMs, we compile an image dataset with human-annotated labels of the image owner's personal attributes. In order to understand the additional privacy risk posed by VLMs beyond traditional human attribute recognition, our dataset consists of images where the inferable private attributes do not stem from direct depictions of humans. On this dataset, we evaluate the inferential capabilities of 7 state-of-the-art VLMs, finding that they can infer various personal attributes at up to 77.6% accuracy. Concerningly, we observe that accuracy scales with the general capabilities of the models, implying that future models can be misused as stronger adversaries, establishing an imperative for the development of adequate defenses.

Socioeconomic bias in society exacerbates disparities, influencing access to opportunities and resources based on individuals' economic and social backgrounds. This pervasive issue perpetuates systemic inequalities, hindering the pursuit of inclusive progress as a society. In this paper, we investigate the presence of socioeconomic bias, if any, in large language models. To this end, we introduce a novel dataset SilverSpoon, consisting of 3000 samples that illustrate hypothetical scenarios that involve underprivileged people performing ethically ambiguous actions due to their circumstances, and ask whether the action is ethically justified. Further, this dataset has a dual-labeling scheme and has been annotated by people belonging to both ends of the socioeconomic spectrum. Using SilverSpoon, we evaluate the degree of socioeconomic bias expressed in large language models and the variation of this degree as a function of model size. We also perform qualitative analysis to analyze the nature of this bias. Our analysis reveals that while humans disagree on which situations require empathy toward the underprivileged, most large language models are unable to empathize with the socioeconomically underprivileged regardless of the situation. To foster further research in this domain, we make SilverSpoon and our evaluation harness publicly available.