A Study on the Framework for Evaluating the Ethics and Trustworthiness of Generative AI

2025-08-30

AI Summary

Existing evaluation methodologies for generative AI predominantly emphasize technical performance while neglecting multidimensional societal impacts—including bias, harm, copyright infringement, privacy violations, and hallucination—posing critical challenges to ethical integrity and trustworthiness. Method: This study introduces the first human-centered, full-lifecycle assessment framework for generative AI, grounded in 11 core principles: fairness, transparency, accountability, privacy, and others. It innovatively integrates comparative analysis of AI ethics policies across major jurisdictions to harmonize cross-regional governance insights. Leveraging interdisciplinary modeling (law, social science, computer science), interpretable evaluation techniques, and a rigorously designed metric system, the framework enables actionable risk identification and mitigation. Contribution/Results: The resulting toolkit provides empirically grounded, operational guidance for policymakers, developers, and end users, advancing responsible innovation and deployment of generative AI systems.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

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.

Problem

• Evaluating ethical challenges in generative AI technologies
• Addressing bias, harmfulness, and privacy violations in AI
• Developing human-centered criteria for AI trustworthiness assessment

Innovation

• Framework for evaluating generative AI ethics
• Human-centered criteria reflecting social impact
• Technical assessments with multidisciplinary perspectives