# Responsible Reporting for Frontier AI Development

arXiv: https://arxiv.org/abs/2404.02675 · HTML: https://mlsanity.com/paper/2404.02675/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 90 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 30 of 36)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2307.15217/) (2023)
- [Unsolved Problems in ML Safety](https://mlsanity.com/paper/2109.13916/) (2021)
- [An Overview of Catastrophic AI Risks](https://mlsanity.com/paper/2306.12001/) (2023)
- [Predictability and Surprise in Large Generative Models](https://mlsanity.com/paper/2202.07785/) (2022)
- [Model evaluation for extreme risks](https://mlsanity.com/paper/2305.15324/) (2023)
- [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020)
- [Sociotechnical Safety Evaluation of Generative AI Systems](https://mlsanity.com/paper/2310.11986/) (2023)
- [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023)
- [Evaluating Frontier Models for Dangerous Capabilities](https://mlsanity.com/paper/2403.13793/) (2024)
- [Black-Box Access is Insufficient for Rigorous AI Audits](https://mlsanity.com/paper/2401.14446/) (2024)
- [Frontier AI Regulation: Managing Emerging Risks to Public Safety](https://mlsanity.com/paper/2307.03718/) (2023)
- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance](https://mlsanity.com/paper/2206.04737/) (2022)
- [On the Societal Impact of Open Foundation Models](https://mlsanity.com/paper/2403.07918/) (2024)
- [Managing extreme AI risks amid rapid progress](https://mlsanity.com/paper/2310.17688/) (2023)
- [A Safe Harbor for AI Evaluation and Red Teaming](https://mlsanity.com/paper/2403.04893/) (2024)
- [AI auditing: The Broken Bus on the Road to AI Accountability](https://mlsanity.com/paper/2401.14462/) (2024)
- [The History and Risks of Reinforcement Learning and Human Feedback](https://mlsanity.com/paper/2310.13595/) (2023)
- [Computing Power and the Governance of Artificial Intelligence](https://mlsanity.com/paper/2402.08797/) (2024)
- [Ecosystem Graphs: The Social Footprint of Foundation Models](https://mlsanity.com/paper/2303.15772/) (2023)
- [Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling](https://mlsanity.com/paper/2402.17861/) (2024)
- [Towards best practices in AGI safety and governance: A survey of expert opinion](https://mlsanity.com/paper/2305.07153/) (2023)
- [The Role of Cooperation in Responsible AI Development](https://mlsanity.com/paper/1907.04534/) (2019)
- [System Safety and Artificial Intelligence](https://mlsanity.com/paper/2202.09292/) (2022)
- [Reward Reports for Reinforcement Learning](https://mlsanity.com/paper/2204.10817/) (2022)

## Built on by (most cited first, 10 of 10)

- [The 2024 Foundation Model Transparency Index](https://mlsanity.com/paper/2407.12929/) (2024)
- [The 2025 Foundation Model Transparency Index](https://mlsanity.com/paper/2512.10169/) (2025)
- [Societal Adaptation to Advanced AI](https://mlsanity.com/paper/2405.10295/) (2024)
- [International Agreements on AI Safety: Review and Recommendations for a Conditional AI Safety Treaty](https://mlsanity.com/paper/2503.18956/) (2025)
- [NeurIPS should lead scientific consensus on AI policy](https://mlsanity.com/paper/2510.00075/) (2025)
- [Standardization Trends on Safety and Trustworthiness Technology for Advanced AI](https://mlsanity.com/paper/2410.22151/) (2024)
- [Incentive Aware AI Regulations: A Credal Characterisation](https://mlsanity.com/paper/2603.05175/) (2026)
- [NeurIPS Should Require Reproducibility Standards for Frontier AI Safety Claims](https://mlsanity.com/paper/2605.08192/) (2026)
- [Plausible Deniability Guarantees for Whistleblowers](https://mlsanity.com/paper/2607.13928/) (2026)
- [Silent Revision: Measuring Undisclosed Change in the Safety Frameworks of Frontier AI Developers](https://mlsanity.com/paper/2609.08789/) (2026)

## Abstract (arXiv)

Mitigating the risks from frontier AI systems requires up-to-date and reliable information about those systems. Organizations that develop and deploy frontier systems have significant access to such information. By reporting safety-critical information to actors in government, industry, and civil society, these organizations could improve visibility into new and emerging risks posed by frontier systems. Equipped with this information, developers could make better informed decisions on risk management, while policymakers could design more targeted and robust regulatory infrastructure. We outline the key features of responsible reporting and propose mechanisms for implementing them in practice.