Several AI chatbots designed for fantasy and sexual role-playing conversations are leaking user prompts to the web in almost real time, new research seen by WIRED shows. Some of the leaked data shows people creating conversations detailing child sexual abuse, according to the research. Conversations with generative AI chatbots are near instantaneous--you type a prompt and the AI responds. If the systems are configured improperly, however, this can lead to chats being exposed. In March, researchers at the security firm UpGuard discovered around 400 exposed AI systems while scanning the web looking for misconfigurations.

In using "LLMs", we are referring to language models which evince in-context learning capabilities (also referred to as "foundation models" [13]). This behavior has traditionally been observed in models with at least a billion parameters.

The advent of large language models (LLMs) and their adoption by the legal community has given rise to the question: what types of legal reasoning can LLMs perform?

The application of AI in education has gained widespread attention for its potential to enhance learning experiences across disciplines, including psychology [1, 2]. In the context of investigative interviewing, especially when questioning suspected child victims, AI offers a promising alternative to traditional training approaches. These conventional methods, often delivered through short workshops, fail to provide the hands-on practice, feedback, and continuous engagement needed for interviewers to master best practices in questioning child victims [3, 4]. Research has shown that while best practices recommend open-ended questions and discourage leading or suggestive queries [5, 6], many interviewers still struggle to implement these techniques effectively during real-world investigations [7]. The adoption of AI-powered child avatars provides a valuable solution, enabling Child Protective Services (CPS) workers to engage in realistic practice sessions without the ethical dilemmas associated with using real children, while simultaneously offering personalized feedback on their performance [8]. Our current system leverages advanced AI techniques within a structured virtual environment to train professionals in investigative interviewing. Specifically, this system integrates the Unity Engine to generate virtual avatars. Despite the potential advantages of our AI-based training system, its effectiveness largely depends on the perceived realism and fidelity of the virtual avatars used in these simulations [9]. Based on our findings, we observed that avatars generated using Generative Adversarial Networks (GANs) demonstrated higher levels of realism compared to those created with the Unity Engine in several key aspects [10].

For the past two years, millions of people searching for child abuse videos on Pornhub's UK website have been interrupted. Each of the 4.4 million times someone has typed in words or phrases linked to abuse, a warning message has blocked the page, saying that kind of content is illegal. And in half the cases, a chatbot has also pointed people to where they can seek help. The warning message and chatbot were deployed by Pornhub as part of a trial program, conducted with two UK-based child protection organizations, to find out whether people could be nudged away from looking for illegal material with small interventions. A new report analyzing the test, shared exclusively with WIRED, says the pop-ups led to a decrease in the number of searches for child sexual abuse material (CSAM) and saw scores of people seek support for their behavior.

Caseworkers in the child welfare (CW) sector use predictive decision-making algorithms built on risk assessment (RA) data to guide and support CW decisions. Researchers have highlighted that RAs can contain biased signals which flatten CW case complexities and that the algorithms may benefit from incorporating contextually rich case narratives, i.e. - casenotes written by caseworkers. To investigate this hypothesized improvement, we quantitatively deconstructed two commonly used RAs from a United States CW agency. We trained classifier models to compare the predictive validity of RAs with and without casenote narratives and applied computational text analysis on casenotes to highlight topics uncovered in the casenotes. Our study finds that common risk metrics used to assess families and build CWS predictive risk models (PRMs) are unable to predict discharge outcomes for children who are not reunified with their birth parent(s). We also find that although casenotes cannot predict discharge outcomes, they contain contextual case signals. Given the lack of predictive validity of RA scores and casenotes, we propose moving beyond quantitative risk assessments for public sector algorithms and towards using contextual sources of information such as narratives to study public sociotechnical systems.

Researchers from the Stanford Internet Observatory say that a dataset used to train AI image generation tools contains at least 1,008 validated instances of child sexual abuse material. The Stanford researchers note that the presence of CSAM in the dataset could allow AI models that were trained on the data to generate new and even realistic instances of CSAM. LAION, the non-profit that created the dataset, told 404 Media that it "has a zero tolerance policy for illegal content and in an abundance of caution, we are temporarily taking down the LAION datasets to ensure they are safe before republishing them." The organization added that, before publishing its datasets in the first place, it created filters to detect and remove illegal content from them. However, 404 points out that LAION leaders have been aware since at least 2021 that there was a possibility of their systems picking up CSAM as they vacuumed up billions of images from the internet.

The advent of large language models (LLMs) and their adoption by the legal community has given rise to the question: what types of legal reasoning can LLMs perform? To enable greater study of this question, we present LegalBench: a collaboratively constructed legal reasoning benchmark consisting of 162 tasks covering six different types of legal reasoning. LegalBench was built through an interdisciplinary process, in which we collected tasks designed and hand-crafted by legal professionals. Because these subject matter experts took a leading role in construction, tasks either measure legal reasoning capabilities that are practically useful, or measure reasoning skills that lawyers find interesting. To enable cross-disciplinary conversations about LLMs in the law, we additionally show how popular legal frameworks for describing legal reasoning -- which distinguish between its many forms -- correspond to LegalBench tasks, thus giving lawyers and LLM developers a common vocabulary. This paper describes LegalBench, presents an empirical evaluation of 20 open-source and commercial LLMs, and illustrates the types of research explorations LegalBench enables.