Failure mode and effects analysis (FMEA) is a critical but labor-intensive process in product development that aims to identify and mitigate potential failure modes to ensure product quality and reliability. In this paper, a novel framework to improve the FMEA process by integrating generative artificial intelligence (AI), in particular large language models (LLMs), is presented. By using these advanced AI tools, we aim to streamline collaborative work in FMEA, reduce manual effort and improve the accuracy of risk assessments. The proposed framework includes LLMs to support data collection, pre-processing, risk identification, and decision-making in FMEA. This integration enables a more efficient and reliable analysis process and leverages the strengths of human expertise and AI capabilities. To validate the framework, we conducted a case study where we first used GPT-3.5 as a proof of concept, followed by a comparison of the performance of three well-known LLMs: GPT-4, GPT-4o and Gemini. These comparisons show significant improvements in terms of speed, accuracy, and reliability of FMEA results compared to traditional methods. Our results emphasize the transformative potential of LLMs in FMEA processes and contribute to more robust design and quality assurance practices. The paper concludes with recommendations for future research focusing on data security and the development of domain-specific LLM training protocols.

Recent studies utilizing AI-driven speech-based Alzheimer’s disease (AD) detection have achieved remarkable success in detecting AD dementia through the analysis of audio and text data. However, detecting AD at an early stage of mild cognitive impairment (MCI), remains a challenging task, due to the lack of sufficient training data and imbalanced diagnostic labels. Motivated by recent advanced developments in Generative AI (GAI) and Large Language Models (LLMs), we propose an LLM-based data generation framework, leveraging prior knowledge encoded in LLMs to generate new data samples. Our novel LLM generation framework introduces two novel data generation strategies, namely, the cross-lingual and the counterfactual data generation, facilitating out-of-distribution learning over new data samples to reduce biases in MCI label prediction due to the systematic underrepresentation of MCI subjects in the AD speech dataset. The results have demonstrated that our proposed framework significantly improves MCI Detection Sensitivity and F1-score on average by a maximum of 38% and 31%, respectively. Furthermore, key speech markers in predicting MCI before and after LLM-based data generation have been identified to enhance our understanding of how the novel data generation approach contributes to the reduction of MCI label prediction biases, shedding new light on speech-based MCI detection under low data resource constraint. Our proposed methodology offers a generalized data generation framework for improving downstream prediction tasks in cases where limited and/or imbalanced data have presented significant challenges to AI-driven health decision-making. Future study can focus on incorporating more datasets and exploiting more acoustic features for speech-based MCI detection.

Generative artificial intelligence (AI) systems, such as large language models, image synthesis tools, and audio generation engines, present remarkable possibilities for creative expression and scientific discovery but also pose pressing challenges for privacy governance. By identifying patterns in vast troves of digital data, these systems can generate hyper-realistic yet fabricated content, surface sensitive inferences about individuals and groups, and shape public discourse at an unprecedented scale. These innovations amplify privacy concerns about nonconsensual data extraction, re-identification, inferential profiling, synthetic media manipulation, algorithmic bias, and quantification. This article argues that the current U.S. legal framework, rooted in a narrowly targeted sectoral approach and overreliance on individual notice and consent, is fundamentally mismatched to address the emergent and systemic privacy harms of generative AI. It examines how the unprecedented scale, speed, and sophistication of these systems strain core assumptions of data protection law, highlighting the misalignment between AI’s societal impacts and individualistic, reactive approaches to privacy governance. The article explores distinctive privacy challenges posed by generative AI, surveys gaps in existing U.S. regulations, and outlines key elements of a new paradigm to protect individual and collective privacy rights that (1) shifts from individual to collective conceptions of privacy; (2) moves from reactive to proactive governance; and (3) reorients the goals and values of AI governance. Despite significant obstacles, it identifies potential policy levers, technical safeguards, and conceptual tools to inform a more proactive and equitable approach to governing generative AI.

Generative artificial intelligence (GenAI) systems generate artwork likely to be copyrightable if made by human. In lieu of discussing whether machine-enabled artworks should be copyrighted, a question that has received a large attention in academic scholarship, this paper proposes to analyse the economic implications of GenAI. Although the valuation of their outputs is still mostly unknown, preliminary studies show that, all other things being equal, humans’ works are evaluated at a significant higher value than machine-enabled ones. Yet, to be properly valued, human-made and machine-enabled products must be distinguishable. They are not. This indistinguishability creates an asymmetry in information that in turn leads to a lemons problem, defined as a market erosion of good-quality products. Against that background, this paper proposes a solution in light of European Union rules of origin and the so-called substantial transformation test. This solution can then be used in copyright law to identify where the human author has been able to make free and creative choices (or lack thereof) required to satisfy the criterion of originality and, hence, copyrightability.

The advent of generative artificial intelligence (AI) models holds potential for aiding teachers in the generation of pedagogical materials. However, numerous knowledge gaps concerning the behavior of these models obfuscate the generation of research-informed guidance for their effective usage. Here, we assess trends in prompt specificity, variability, and weaknesses in foreign language teacher lesson plans generated by zero-shot prompting in ChatGPT. Iterating a series of prompts that increased in complexity, we found that output lesson plans were generally high quality, though additional context and specificity to a prompt did not guarantee a concomitant increase in quality. Additionally, we observed extreme cases of variability in outputs generated by the same prompt. In many cases, this variability reflected a conflict between outdated (e.g. reciting scripted dialogues) and more current research-based pedagogical practices (e.g. a focus on communication). These results suggest that the training of generative AI models on classic texts concerning pedagogical practices may bias generated content toward teaching practices that have been long refuted by research. Collectively, our results offer immediate translational implications for practicing and training foreign language teachers on the use of AI tools. More broadly, these findings highlight trends in generative AI output that have implications for the development of pedagogical materials across a diversity of content areas.

With the recent advances in artificial intelligence (AI), patients are increasingly exposed to misleading medical information. Generative AI models, including large language models such as ChatGPT, create and modify text, images, audio and video information based on training data. Commercial use of generative AI is expanding rapidly and the public will routinely receive messages created by generative AI. However, generative AI models may be unreliable, routinely make errors and widely spread misinformation. Misinformation created by generative AI about mental illness may include factual errors, nonsense, fabricated sources and dangerous advice. Psychiatrists need to recognise that patients may receive misinformation online, including about medicine and psychiatry.