Epistemic injustice related to AI is a growing concern. In relation to machine learning models, epistemic injustice can have a diverse range of sources, ranging from epistemic opacity, the discriminatory automation of testimonial prejudice, and the distortion of human beliefs via generative AI's hallucinations to the exclusion of the global South in global AI governance, the execution of bureaucratic violence via algorithmic systems, and interactions with conversational artificial agents. Based on a proposed general taxonomy of epistemic injustice, this paper first sketches a taxonomy of the types of epistemic injustice in the context of AI, relying on the work of scholars from the fields of philosophy of technology, political philosophy and social epistemology. Secondly, an additional conceptualization on epistemic injustice in the context of AI is provided: generative hermeneutical erasure. I argue that this injustice the automation of 'epistemicide', the injustice done to epistemic agents in their capacity for collective sense-making through the suppression of difference in epistemology and conceptualization by LLMs. AI systems' 'view from nowhere' epistemically inferiorizes non-Western epistemologies and thereby contributes to the erosion of their epistemic particulars, gradually contributing to hermeneutical erasure. This work's relevance lies in proposal of a taxonomy that allows epistemic injustices to be mapped in the AI domain and the proposal of a novel form of AI-related epistemic injustice.

Generative AI, including large language models (LLMs) have the potential -- and already are being used -- to increase the speed, scale, and types of unsafe conversations online. LLMs lower the barrier for entry for bad actors to create unsafe conversations in particular because of their ability to generate persuasive and human-like text. In our current work, we explore ways to promote online safety by teaching people about unsafe conversations that can occur online with and without LLMs. We build on prior work that shows that LLMs can successfully simulate scam conversations. We also leverage research in the learning sciences that shows that providing feedback on one's hypothetical actions can promote learning. In particular, we focus on simulating scam conversations using LLMs. Our work incorporates two LLMs that converse with each other to simulate realistic, unsafe conversations that people may encounter online between a scammer LLM and a target LLM but users of our system are asked provide feedback to the target LLM.

OpenAI has launched a new "study mode" for ChatGPT that's designed to help students better understand complex topics--but instead of dishing out direct answers, study mode employs the Socratic method to ask questions and guide users to finding those answers. Or another way to look at it: in study mode conversations, ChatGPT gradually rolls out information to the user in stages to avoid overloading and overwhelming, and to prevent the AI chatbot from doing all the work on the user's behalf. According to OpenAI, study mode was developed in collaboration with teachers, researchers, and education experts. It's based on customized system instructions rather than an entirely new AI model. Study mode will first be available to users on ChatGPT Free, Plus, Pro, and Team plans. ChatGPT Edu users will get access within a few weeks.

A baby boy has just won the new record for the "oldest baby." Thaddeus Daniel Pierce, who arrived on July 26, developed from an embryo that had been in storage for 30 and a half years. Lindsey and her husband, Tim Pierce, who live in London, Ohio, "adopted" the embryo from Linda Archerd, who had it created in 1994. The couple, aged 35 and 34, respectively, had been trying for a baby for seven years. OpenAI is launching Study Mode, a version of ChatGPT for college students that it promises will act less like a lookup tool and more like a friendly, always-available tutor.

The below article first appeared in David Corn's newsletter, Our Land. The newsletter comes out twice a week (most of the time) and provides behind-the-scenes stories and articles about politics, media, and culture. Subscribing costs just 5 a month--but you can sign up for a free 30-day trial. There are only a few potential existential threats to human society, as far as we know. Nuclear weapons are the most obvious.

The rise of generative AI, has driven significant advancements in high-risk sectors like healthcare and finance. The Retrieval-Augmented Generation (RAG) architecture, combining language models (LLMs) with search engines, is particularly notable for its ability to generate responses from document corpora. Despite its potential, the reliability of RAG systems in critical contexts remains a concern, with issues such as hallucinations persisting. This study evaluates a RAG system used in due diligence for an investment fund. We propose a robust evaluation protocol combining human annotations and LLM-Judge annotations to identify system failures, like hallucinations, off-topic, failed citations, and abstentions. Inspired by the Prediction Powered Inference (PPI) method, we achieve precise performance measurements with statistical guarantees. We provide a comprehensive dataset for further analysis. Our contributions aim to enhance the reliability and scalability of RAG systems evaluation protocols in industrial applications.

As Artificial Intelligence (AI) tools become increasingly embedded in higher education, understanding how students interact with these systems is essential to supporting effective learning. This study examines how students' AI literacy and prior exposure to AI technologies shape their perceptions of Socratic Mind, an interactive AI-powered formative assessment tool. Drawing on Self-Determination Theory and user experience research, we analyze relationships among AI literacy, perceived usability, satisfaction, engagement, and perceived learning effectiveness. Data from 309 undergraduates in Computer Science and Business courses were collected through validated surveys. Partial least squares structural equation modeling showed that AI literacy - especially self-efficacy, conceptual understanding, and application skills - significantly predicts usability, satisfaction, and engagement. Usability and satisfaction, in turn, strongly predict perceived learning effectiveness, while prior AI exposure showed no significant effect. These findings highlight that AI literacy, rather than exposure alone, shapes student experiences. Designers should integrate adaptive guidance and user-centered features to support diverse literacy levels, fostering inclusive, motivating, and effective AI-based learning environments.

This paper presents the development of a generative adversarial network (GAN) for synthesizing dental panoramic radiographs. Although exploratory in nature, the study aims to address the scarcity of data in dental research and education. We trained a deep convolutional GAN (DCGAN) using a Wasserstein loss with gradient penalty (WGANGP) on a dataset of 2322 radiographs of varying quality. The focus was on the dentoalveolar regions, other anatomical structures were cropped out. Extensive preprocessing and data cleaning were performed to standardize the inputs while preserving anatomical variability. We explored four candidate models by varying critic iterations, feature depth, and the use of denoising prior to training. A clinical expert evaluated the generated radiographs based on anatomical visibility and realism, using a 5-point scale (1 very poor 5 excellent). Most images showed moderate anatomical depiction, although some were degraded by artifacts. A trade-off was observed the model trained on non-denoised data yielded finer details especially in structures like the mandibular canal and trabecular bone, while a model trained on denoised data offered superior overall image clarity and sharpness. These findings provide a foundation for future work on GAN-based methods in dental imaging.

This paper explores the use of large language models (LLMs) to assist in the development of new disease modules for Synthea, an open-source synthetic health data generator. Incorporating LLMs into the module development process has the potential to reduce development time, reduce required expertise, expand model diversity, and improve the overall quality of synthetic patient data. We demonstrate four ways that LLMs can support Synthea module creation: generating a disease profile, generating a disease module from a disease profile, evaluating an existing Synthea module, and refining an existing module. We introduce the concept of progressive refinement, which involves iteratively evaluating the LLM-generated module by checking its syntactic correctness and clinical accuracy, and then using that information to modify the module. While the use of LLMs in this context shows promise, we also acknowledge the challenges and limitations, such as the need for human oversight, the importance of rigorous testing and validation, and the potential for inaccuracies in LLM-generated content. The paper concludes with recommendations for future research and development to fully realize the potential of LLM-aided synthetic data creation.

Conversational agents (CA s) based on generative artificial intelligence frequently face challenges ensuring ethical interactions that align with human values. Current value alignment efforts largely rely on top - down approaches, such as technical guidelines or legal value principles. However, these methods tend to be disconnec ted from the specific contexts in which CAs operate, potentially leading to misalignment with users' interests. To address this challenge, we propose a novel, bottom - up approach to value alignment, utilizing the value ontology of the ISO Value - Based Engine ering standard for ethical IT design. We analyse 593 ethically sensitive system outputs identified from 16,908 conversational logs of a major European employment service CA to identify core values and instances of value misalignment within real - world inter actions. The results revealed nine core values and 32 different value misalignments that negatively impacted users. Our findings provide actionable insights for CA providers seeking to address ethical challenges and achieve more context - sensitive value ali gnment.