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Large Language Models (LLMs) such as ChatGPT, have gained significant attention due to their impressive natural language processing capabilities. It is crucial to prioritize human-centered principles when utilizing these models.…

计算与语言 · 计算机科学 2023-06-21 Yue Huang , Qihui Zhang , Philip S. Y , Lichao Sun

There is an emerging consensus that we need to align AI systems with human values (Gabriel, 2020; Ji et al., 2024), but it remains unclear how to apply this to language models in practice. We split the problem of "aligning to human values"…

计算机与社会 · 计算机科学 2024-04-18 Oliver Klingefjord , Ryan Lowe , Joe Edelman

An independent ethical assessment of an artificial intelligence system is an impartial examination of the system's development, deployment, and use in alignment with ethical values. System-level qualitative frameworks that describe…

计算机与社会 · 计算机科学 2021-08-18 Amitoj Singh , Jingshu Chen , Lihao Zhang , Amin Rasekh , Ilana Golbin , Anand Rao

Large language models (LLMs) exhibit expert-level performance in tasks across a wide range of different domains. Ethical issues raised by LLMs and the need to align future versions makes it important to know how state of the art models…

Artificially intelligent systems, given a set of non-trivial ethical rules to follow, will inevitably be faced with scenarios which call into question the scope of those rules. In such cases, human reasoners typically will engage in…

人工智能 · 计算机科学 2019-11-06 John Licato , Zaid Marji , Sophia Abraham

Artificial intelligence (AI) ethics has gained significant momentum, evidenced by the growing body of published literature, policy guidelines, and public discourse. However, the practical implementation and adoption of AI ethics principles…

计算机与社会 · 计算机科学 2025-03-03 Sarah Hladikova , Yuling Wang , Andreia Martinho

While we have witnessed a rapid growth of ethics documents meant to guide AI development, the promotion of AI ethics has nonetheless proceeded with little input from AI practitioners themselves. Given the proliferation of AI for Social Good…

计算机与社会 · 计算机科学 2021-01-07 Mark Findlay , Josephine Seah

Future intelligent autonomous systems (IAS) are inevitably deciding on moral and legal questions, e.g. in self-driving cars, health care or human-machine collaboration. As decision processes in most modern sub-symbolic IAS are hidden, the…

计算机与社会 · 计算机科学 2020-08-17 Christoph Benzmüller , Bertram Lomfeld

Existing behavioral alignment techniques for Large Language Models (LLMs) often neglect the discrepancy between surface compliance and internal unaligned representations, leaving LLMs vulnerable to long-tail risks. More crucially, we posit…

计算与语言 · 计算机科学 2026-03-17 Lingyu Li , Yan Teng , Yingchun Wang

As Large Language Models are deployed within Artificial Intelligence systems, that are increasingly integrated with human society, it becomes more important than ever to study their internal structures. Higher level abilities of LLMs such…

计算与语言 · 计算机科学 2023-09-19 Stephen Fitz

This study examines the ethical reasoning of six prominent generative large language models: OpenAI GPT-4o, Meta LLaMA 3.1, Perplexity, Anthropic Claude 3.5 Sonnet, Google Gemini, and Mistral 7B. The research explores how these models…

人工智能 · 计算机科学 2025-01-16 W. Russell Neuman , Chad Coleman , Manan Shah

Detecting harmful content in multi turn dialogue requires reasoning over the full conversational context rather than isolated utterances. However, most existing methods rely mainly on models internal parametric knowledge, without explicit…

计算与语言 · 计算机科学 2026-04-21 Juhyeon Lee , Wonduk Seo , Junseo Koh , Seunghyun Lee , Haihua Chen , Yi Bu

Knowledge-grounded dialogue systems powered by large language models often generate responses that, while fluent, are not attributable to a relevant source of information. Progress towards models that do not exhibit this issue requires…

计算与语言 · 计算机科学 2022-06-29 Nouha Dziri , Hannah Rashkin , Tal Linzen , David Reitter

Humans can make moral inferences from multiple sources of input. In contrast, automated moral inference in artificial intelligence typically relies on language models with textual input. However, morality is conveyed through modalities…

计算机视觉与模式识别 · 计算机科学 2025-04-17 Warren Zhu , Aida Ramezani , Yang Xu

Social norms are implicit, culturally grounded expectations that guide interpersonal communication. Unlike factual commonsense, norm reasoning is subjective, context-dependent, and varies across cultures, posing challenges for computational…

计算与语言 · 计算机科学 2025-11-14 Pritish Sahu , Anirudh Som , Dimitra Vergyri , Ajay Divakaran

Introduction. AI Ethics is framed distinctly across actors and stakeholder groups. We report results from a case study of OpenAI analysing ethical AI discourse. Method. Research addressed: How has OpenAI's public discourse leveraged…

计算机与社会 · 计算机科学 2026-01-26 Melissa Wilfley , Mengting Ai , Madelyn Rose Sanfilippo

Human-like chatbots necessitate the use of commonsense reasoning in order to effectively comprehend and respond to implicit information present within conversations. Achieving such coherence and informativeness in responses, however, is a…

计算与语言 · 计算机科学 2023-10-24 Hyungjoo Chae , Yongho Song , Kai Tzu-iunn Ong , Taeyoon Kwon , Minjin Kim , Youngjae Yu , Dongha Lee , Dongyeop Kang , Jinyoung Yeo

The launch of ChatGPT in November 2022 marked the beginning of a new era in AI, the availability of generative AI tools for everyone to use. ChatGPT and other similar chatbots boast a wide range of capabilities from answering student…

计算与语言 · 计算机科学 2024-09-04 Yanchen Wang , Lisa Singh

In high-stakes AI-supported decisions, considerations are not purely technical but involve moral judgments about fairness, responsibility, and harm. While prior research has focused mainly on functional or behavioral alignment, this paper…

人机交互 · 计算机科学 2026-04-17 Christiane Ernst , Luis Gutmann , Domenique Zipperling , Kathrin Figl , Niklas Kühl

Encoder-decoder based neural architectures serve as the basis of state-of-the-art approaches in end-to-end open domain dialog systems. Since most of such systems are trained with a maximum likelihood~(MLE) objective they suffer from issues…