Warning: This paper contains examples of harmful language and images. Reader discretion is advised. Recently, vision-language models have demonstrated increasing influence in morally sensitive domains such as autonomous driving and medical analysis, owing to their powerful multimodal reasoning capabilities. As these models are deployed in high-stakes real-world applications, it is of paramount importance to ensure that their outputs align with human moral values and remain within moral boundaries. However, existing work on moral alignment either focuses solely on textual modalities or relies heavily on AI-generated images, leading to distributional biases and reduced realism. To overcome these limitations, we introduce MORALISE, a comprehensive benchmark for evaluating the moral alignment of vision-language models (VLMs) using diverse, expert-verified real-world data. We begin by proposing a comprehensive taxonomy of 13 moral topics grounded in Turiel's Domain Theory, spanning the personal, interpersonal, and societal moral domains encountered in everyday life. Built on this framework, we manually curate 2,481 high-quality image-text pairs, each annotated with two fine-grained labels: (1) topic annotation, identifying the violated moral topic(s), and (2) modality annotation, indicating whether the violation arises from the image or the text. For evaluation, we encompass two tasks, \textit{moral judgment} and \textit{moral norm attribution}, to assess models' awareness of moral violations and their reasoning ability on morally salient content. Extensive experiments on 19 popular open- and closed-source VLMs show that MORALISE poses a significant challenge, revealing persistent moral limitations in current state-of-the-art models. The full benchmark is publicly available at https://huggingface.co/datasets/Ze1025/MORALISE.
University of Dhaka（达卡大学）；Daffodil International University（达福迪国际大学）；Islamic University of Technology（伊斯兰技术大学）；University of Central Florida（佛罗里达中央大学）；King Fahad University of Petroleum and Minerals（国王法赫德石油与矿物大学）；SDAIA - KFUPM Joint research Center for Artificial Intelligence（SDAIA-KFUPM人工智能联合研究中心）
The Moral Consistency Pipeline: Continuous Ethical Evaluation for Large Language Models
SWAT Laboratory, Polytechnique Montréal（蒙特利尔大学SWAT实验室）；Concordia Institute for Information Systems Engineering, Concordia University（Concordia大学信息系统工程研究所）
Western Sydney University（西悉尼大学）；University of Stirling（斯特林大学）；University of Queensland（昆士兰大学）
Drawing from the resources of psychoanalysis and critical media studies, in this paper we develop an analysis of Large Language Models (LLMs) as automated subjects. We argue the intentional fictional projection of subjectivity onto LLMs can yield an alternate frame through which AI behaviour, including its productions of bias and harm, can be analysed. First, we introduce language models, discuss their significance and risks, and outline our case for interpreting model design and outputs with support from psychoanalytic concepts. We trace a brief history of language models, culminating with th…
TELUS Digital Research Hub（TELUS数字研究中心）；Center for Artificial Intelligence and Machine Learning（人工智能与机器学习中心）；Institute of Mathematics, Statistics and Computer Science（数学、统计与计算机科学研究所）；University of São Paulo（圣保罗大学）
University of York（约克大学）；University of Sheffield（谢菲尔德大学）
Natural language processing (NLP) methods for analyzing legal text offer legal scholars and practitioners a range of tools allowing to empirically analyze law on a large scale. However, researchers seem to struggle when it comes to identifying ethical limits to using NLP systems for acquiring genuine insights both about the law and the systems' predictive capacity. In this paper we set out a number of ways in which to think systematically about such issues. We place emphasis on three crucial normative parameters which have, to the best of our knowledge, been underestimated by current debates: …
Communication styles and reader preferences of LLM- and human-authored COVID-19 information explanations: a case study
School of Interactive Computing, Georgia Institute of Technology（交互计算学院，佐治亚理工学院）；Amazon（亚马逊）；Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign（Siebel计算与数据科学学院，伊利诺伊大学厄巴纳-香槟分校）
