A Study on the Framework for Evaluating the Ethics and Trustworthiness of Generative AI
Not on the map. Why?
Abstract (arXiv)
This study provides an in_depth analysis of the ethical and trustworthiness challenges emerging alongside the rapid advancement of generative artificial intelligence (AI) technologies and proposes a comprehensive framework for their systematic evaluation. While generative AI, such as ChatGPT, demonstrates remarkable innovative potential, it simultaneously raises ethical and social concerns, including bias, harmfulness, copyright infringement, privacy violations, and hallucination. Current AI evaluation methodologies, which mainly focus on performance and accuracy, are insufficient to address these multifaceted issues. Thus, this study emphasizes the need for new human_centered criteria that also reflect social impact. To this end, it identifies key dimensions for evaluating the ethics and trustworthiness of generative AI_fairness, transparency, accountability, safety, privacy, accuracy, consistency, robustness, explainability, copyright and intellectual property protection, and source traceability and develops detailed indicators and assessment methodologies for each. Moreover, it provides a comparative analysis of AI ethics policies and guidelines in South Korea, the United States, the European Union, and China, deriving key approaches and implications from each. The proposed framework applies across the AI lifecycle and integrates technical assessments with multidisciplinary perspectives, thereby offering practical means to identify and manage ethical risks in real_world contexts. Ultimately, the study establishes an academic foundation for the responsible advancement of generative AI and delivers actionable insights for policymakers, developers, users, and other stakeholders, supporting the positive societal contributions of AI technologies.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 10 of its 57 references that are in the map. First posted on arXiv 30 Aug 2025.
Builds on
10 of 57 references.
References · 10
- 2017Attention Is All You Needcited by 60,361
- 2022Generative Adversarial Networkscited by 5,627
- 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
- 2023AgentBench: Evaluating LLMs as Agentscited by 1,267
- 2023Foundation Models for Decision Making: Problems, Methods, and Opportunitiescited by 164
- 2023FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Modelscited by 46
- 2023A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecturecited by 28
- 2024Who Followed the Blueprint? Analyzing the Responses of U.S. Federal Agencies to the Blueprint for an AI Bill of Rightscited by 11
- 2025Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platformcited by 5
- 2025E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processingcited by 5
Built on by
Citing papers per year: 2026: 2.
2 papers.