A growing number of AI tools are being developed for the legal sector, to help professionals search lengthy texts or check court rulings. Leiden SAILS researcher Masha Medvedeva, an expert on the technical development of these systems, warns: "Users should know what's under the hood." I have technical expertise on building AI systems and I've been embedded in various law faculties. My research is focused on technical design choices in systems that may have downstream implications on whoever is going to use them. These choices can have implications for law as a whole, for legal practice or for individuals.

GM's Cruise self-driving car unit on Thursday revealed US Department of Justice and Securities and Exchange Commission probes stemming from an October collision in which one of its autonomous vehicles dragged a pedestrian who had been struck by another vehicle. Cruise reported the government investigations in a blog post in which the company also vowed to reform its culture stemming from a "failure of leadership" around the incident. The blog post did not disclose the status of the victim, who was dragged 20ft by the vehicle, nor the scope of the justice department and SEC probes. Cruise's four-page post cited "inadequate and uncoordinated internal processes, mistakes in judgment, an'us versus them' mentality with government officials, and a fundamental misunderstanding of regulatory requirements and expectations". More than 100 people knew details of the incident prior to Cruise's meetings with regulators, the report found.

Deepfake technology tools have become ubiquitous, "democratizing" the ability to manipulate images and videos. One popular use of such technology is the creation of sexually explicit content, which can then be posted and shared widely on the internet. This article examines attitudes and behaviors related to non-consensual synthetic intimate imagery (NSII) across over 16,000 respondents in 10 countries. Despite nascent societal awareness of NSII, NSII behaviors were considered harmful. In regards to prevalence, 2.2% of all respondents indicated personal victimization, and 1.8% all of respondents indicated perpetration behaviors. Respondents from countries with relevant legislation also reported perpetration and victimization experiences, suggesting legislative action alone is not a sufficient solution to deter perpetration. Technical considerations to reduce harms may include suggestions for how individuals can better monitor their presence online, as well as enforced platform policies which ban, or allow for removal of, NSII content.

Regulatory compliance in the pharmaceutical industry entails navigating through complex and voluminous guidelines, often requiring significant human resources. To address these challenges, our study introduces a chatbot model that utilizes generative AI and the Retrieval Augmented Generation (RAG) method. This chatbot is designed to search for guideline documents relevant to the user inquiries and provide answers based on the retrieved guidelines. Recognizing the inherent need for high reliability in this domain, we propose the Question and Answer Retrieval Augmented Generation (QA-RAG) model. In comparative experiments, the QA-RAG model demonstrated a significant improvement in accuracy, outperforming all other baselines including conventional RAG methods. This paper details QA-RAG's structure and performance evaluation, emphasizing its potential for the regulatory compliance domain in the pharmaceutical industry and beyond. We have made our work publicly available for further research and development.

Recently, numerous highly-valuable Deep Neural Networks (DNNs) have been trained using deep learning algorithms. To protect the Intellectual Property (IP) of the original owners over such DNN models, backdoor-based watermarks have been extensively studied. However, most of such watermarks fail upon model extraction attack, which utilizes input samples to query the target model and obtains the corresponding outputs, thus training a substitute model using such input-output pairs. In this paper, we propose a novel watermark to protect IP of DNN models against model extraction, named MEA-Defender. In particular, we obtain the watermark by combining two samples from two source classes in the input domain and design a watermark loss function that makes the output domain of the watermark within that of the main task samples. Since both the input domain and the output domain of our watermark are indispensable parts of those of the main task samples, the watermark will be extracted into the stolen model along with the main task during model extraction. We conduct extensive experiments on four model extraction attacks, using five datasets and six models trained based on supervised learning and self-supervised learning algorithms. The experimental results demonstrate that MEA-Defender is highly robust against different model extraction attacks, and various watermark removal/detection approaches.

In this paper, we describe our approaches and results for Task 2 of the LT-EDI 2024 Workshop, aimed at detecting homophobia and/or transphobia across ten languages. Our methodologies include monolingual transformers and ensemble methods, capitalizing on the strengths of each to enhance the performance of the models. The ensemble models worked well, placing our team, MasonTigers, in the top five for eight of the ten languages, as measured by the macro F1 score. Our work emphasizes the efficacy of ensemble methods in multilingual scenarios, addressing the complexities of language-specific tasks.

This paper introduces a Korean legal judgment prediction (LJP) dataset for insurance disputes. Successful LJP models on insurance disputes can benefit insurance companies and their customers. It can save both sides' time and money by allowing them to predict how the result would come out if they proceed to the dispute mediation process. As is often the case with low-resource languages, there is a limitation on the amount of data available for this specific task. To mitigate this issue, we investigate how one can achieve a good performance despite the limitation in data. In our experiment, we demonstrate that Sentence Transformer Fine-tuning (SetFit, Tunstall et al., 2022) is a good alternative to standard fine-tuning when training data are limited. The models fine-tuned with the SetFit approach on our data show similar performance to the Korean LJP benchmark models (Hwang et al., 2022) despite the much smaller data size.

Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence.

The Department of Justice and the Securities and Exchange Commission have opened an investigation into General Motors-owned autonomous car company Cruise, following an October incident here where one of its cars hit a jaywalking pedestrian and dragged her about 20 feet.

The Federal Trade Commission is launching an inquiry into massive investments made by Microsoft, Amazon and Alphabet into generative AI startups OpenAI and Anthropic, the agency announced on Thursday. The FTC said that it had issued "compulsory orders" to the companies and would scrutinize their relationships with AI startups to understand their impact on competition. "History shows that new technologies can create new markets and healthy competition," FTC Chair Lina Khan said in a statement. "As companies race to develop and monetize AI, we must guard against tactics that foreclose this opportunity. Our study will shed light on whether investments and partnerships pursued by dominant companies risk distorting innovation and undermining fair competition."