# Societal Adaptation to Advanced AI

arXiv: https://arxiv.org/abs/2405.10295 · HTML: https://mlsanity.com/paper/2405.10295/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 109 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 27 of 27)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [WildChat: 1M ChatGPT Interaction Logs in the Wild](https://mlsanity.com/paper/2405.01470/) (2024)
- [Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models](https://mlsanity.com/paper/2310.02949/) (2023)
- [Measuring Progress on Scalable Oversight for Large Language Models](https://mlsanity.com/paper/2211.03540/) (2022)
- [An Overview of Catastrophic AI Risks](https://mlsanity.com/paper/2306.12001/) (2023)
- [Sociotechnical Safety Evaluation of Generative AI Systems](https://mlsanity.com/paper/2310.11986/) (2023)
- [LLM Agents can Autonomously Exploit One-day Vulnerabilities](https://mlsanity.com/paper/2404.08144/) (2024)
- [Is Power-Seeking AI an Existential Risk?](https://mlsanity.com/paper/2206.13353/) (2022)
- [Frontier AI Regulation: Managing Emerging Risks to Public Safety](https://mlsanity.com/paper/2307.03718/) (2023)
- [Deep Neural Network Fingerprinting by Conferrable Adversarial Examples](https://mlsanity.com/paper/1912.00888/) (2019)
- [Visibility into AI Agents](https://mlsanity.com/paper/2401.13138/) (2024)
- [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)
- [The Gradient of Generative AI Release: Methods and Considerations](https://mlsanity.com/paper/2302.04844/) (2023)
- [Escalation Risks from Language Models in Military and Diplomatic Decision-Making](https://mlsanity.com/paper/2401.03408/) (2024)
- [Safety Cases: How to Justify the Safety of Advanced AI Systems](https://mlsanity.com/paper/2403.10462/) (2024)
- [People cannot distinguish GPT-4 from a human in a Turing test](https://mlsanity.com/paper/2405.08007/) (2024)
- [Structured access: an emerging paradigm for safe AI deployment](https://mlsanity.com/paper/2201.05159/) (2022)
- [Computing Power and the Governance of Artificial Intelligence](https://mlsanity.com/paper/2402.08797/) (2024)
- [BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13B](https://mlsanity.com/paper/2311.00117/) (2023)
- [As Good As A Coin Toss: Human detection of AI-generated images, videos, audio, and audiovisual stimuli](https://mlsanity.com/paper/2403.16760/) (2024)
- [Birdwatch: Crowd Wisdom and Bridging Algorithms can Inform Understanding and Reduce the Spread of Misinformation](https://mlsanity.com/paper/2210.15723/) (2022)
- [Protecting Society from AI Misuse: When are Restrictions on Capabilities Warranted?](https://mlsanity.com/paper/2303.09377/) (2023)
- [Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Framework](https://mlsanity.com/paper/2311.14711/) (2023)
- [Responsible Reporting for Frontier AI Development](https://mlsanity.com/paper/2404.02675/) (2024)
- [AI and Shared Prosperity](https://mlsanity.com/paper/2105.08475/) (2021)

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

- [Infrastructure for AI Agents](https://mlsanity.com/paper/2501.10114/) (2025)
- [Training Compute Thresholds: Features and Functions in AI Regulation](https://mlsanity.com/paper/2405.10799/) (2024)
- [The Singapore Consensus on Global AI Safety Research Priorities](https://mlsanity.com/paper/2506.20702/) (2025)
- [Measuring AI R&D Automation](https://mlsanity.com/paper/2603.03992/) (2026)
- [Misalignment or misuse? The AGI alignment tradeoff](https://mlsanity.com/paper/2506.03755/) (2025)
- [Against racing to AGI: Cooperation, deterrence, and catastrophic risks](https://mlsanity.com/paper/2507.21839/) (2025)
- [From Turing to Tomorrow: The UK's Approach to AI Regulation](https://mlsanity.com/paper/2507.03050/) (2025)
- [Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks](https://mlsanity.com/paper/2508.06411/) (2025)
- [Societal Capacity Assessment Framework: Measuring Resilience to Inform Advanced AI Risk Management](https://mlsanity.com/paper/2509.22742/) (2025)
- [Comprehensive AI governance requires addressing non-model gains](https://mlsanity.com/paper/2606.00047/) (2026)

## Abstract (arXiv)

Existing strategies for managing risks from advanced AI systems often focus on affecting what AI systems are developed and how they diffuse. However, this approach becomes less feasible as the number of developers of advanced AI grows, and impedes beneficial use-cases as well as harmful ones. In response, we urge a complementary approach: increasing societal adaptation to advanced AI, that is, reducing the expected negative impacts from a given level of diffusion of a given AI capability. We introduce a conceptual framework which helps identify adaptive interventions that avoid, defend against and remedy potentially harmful uses of AI systems, illustrated with examples in election manipulation, cyberterrorism, and loss of control to AI decision-makers. We discuss a three-step cycle that society can implement to adapt to AI. Increasing society's ability to implement this cycle builds its resilience to advanced AI. We conclude with concrete recommendations for governments, industry, and third-parties.