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

arXiv: https://arxiv.org/abs/2509.00398 · HTML: https://mlsanity.com/paper/2509.00398/

## 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 (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [AgentBench: Evaluating LLMs as Agents](https://mlsanity.com/paper/2308.03688/) (2023)
- [Foundation Models for Decision Making: Problems, Methods, and Opportunities](https://mlsanity.com/paper/2303.04129/) (2023)
- [FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Models](https://mlsanity.com/paper/2311.01477/) (2023)
- [A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture](https://mlsanity.com/paper/2309.01105/) (2023)
- [Who Followed the Blueprint? Analyzing the Responses of U.S. Federal Agencies to the Blueprint for an AI Bill of Rights](https://mlsanity.com/paper/2404.19076/) (2024)
- [Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform](https://mlsanity.com/paper/2501.00750/) (2025)
- [E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processing](https://mlsanity.com/paper/2505.20733/) (2025)

## Built on by (most cited first, 2 of 2)

- [A Self-Healing Framework for Reliable LLM-Based Autonomous Agents](https://mlsanity.com/paper/2605.06737/) (2026)
- [Agent Economics: An Entropy-Controlled Pluralistic Alignment Framework for Preventing Artificial Hivemind in Autonomous Agents](https://mlsanity.com/paper/2606.09039/) (2026)

## 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.