The rapid and wide-scale adoption of AI to generate human speech poses a range of significant ethical and safety risks to society that need to be addressed. For example, a growing number of speech generation incidents are associated with swatting attacks in the United States, where anonymous perpetrators create synthetic voices that call police officers to close down schools and hospitals, or to violently gain access to innocent citizens' homes. Incidents like this demonstrate that multimodal generative AI risks and harms do not exist in isolation, but arise from the interactions of multiple stakeholders and technical AI systems. In this paper we analyse speech generation incidents to study how patterns of specific harms arise. We find that specific harms can be categorised according to the exposure of affected individuals, that is to say whether they are a subject of, interact with, suffer due to, or are excluded from speech generation systems. Similarly, specific harms are also a consequence of the motives of the creators and deployers of the systems. Based on these insights we propose a conceptual framework for modelling pathways to ethical and safety harms of AI, which we use to develop a taxonomy of harms of speech generators. Our relational approach captures the complexity of risks and harms in sociotechnical AI systems, and yields a taxonomy that can support appropriate policy interventions and decision making for the responsible development and release of speech generation models.
When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance
School of Law, China University of Political Science and Law（中国政法大学法学院）；Zhejiang University of Finance and Economics Dongfang College（浙江财经大学东洋学院）；School of Information Management for Law, China University of Political Science and Law（中国政法大学信息管理法学院）；Department of Artificial Intelligence, Chung-Ang University（成均馆大学人工智能系）；Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University（香港理工大学数据科学与人工智能系）
Enhancing AI-Driven Education: Integrating Cognitive Frameworks, Linguistic Feedback Analysis, and Ethical Considerations for Improved Content Generation
Learning, Data and Robotics (LDR) ESIEA Lab（学习、数据与机器人（LDR）ESIEA实验室）；Department of Computer Engineering（计算机工程系）；Graduate School of Management and Innovation（管理与创新研究生院）；Faculty of Science（科学学院）；Lebanese University（黎巴嫩大学）
DeepMind；California Institute of Technology（加州理工学院）；University of Toronto（多伦多大学）；University College Dublin（都柏林大学学院）
This paper aims to help structure the risk landscape associated with large-scale Language Models (LMs). In order to foster advances in responsible innovation, an in-depth understanding of the potential risks posed by these models is needed. A wide range of established and anticipated risks are analysed in detail, drawing on multidisciplinary expertise and literature from computer science, linguistics, and social sciences. We outline six specific risk areas: I. Discrimination, Exclusion and Toxicity, II. Information Hazards, III. Misinformation Harms, V. Malicious Uses, V. Human-Computer Intera…
Natural language generation models are computer systems that generate coherent language when prompted with a sequence of words as context. Despite their ubiquity and many beneficial applications, language generation models also have the potential to inflict social harms by generating discriminatory language, hateful speech, profane content, and other harmful material. Ethical assessment of these models is therefore critical. But it is also a challenging task, requiring an expertise in several specialized domains, such as computational linguistics and social justice. While significant strides h…
A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models
ELLIS Alicante（欧洲学习与智能系统实验室阿利坎特分部）；Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague（捷克布拉格技术大学捷克信息学、机器人学与控制论研究所）；Delft University of Technology（代尔夫特理工大学）
Role-Play Paradox in Large Language Models: Reasoning Performance Gains and Ethical Dilemmas
University of Toronto（多伦多大学）；University of Alberta（阿尔伯塔大学）；University of Waterloo（滑铁卢大学）；Mcgill University（麦吉尔大学）；Renmin University of China（中国人民大学）；The Australian National University（澳大利亚国立大学）
First, Do No Harm: AI Supervisor Scaffolds Novice Growth in Counselor Education
Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Ministry of Education（教育部脑健康智能评估与干预重点实验室）；School of Medical Science and Engineering, Beijing Institute of Technology（北京理工大学医学科学与工程学院）；Chinese People’s Liberation Army General Hospital（中国人民解放军总医院）；Lanzhou University（兰州大学）；Hebei University（河北大学）
