mlsanityData to 7 Oct 2026

Protecting Society from AI Misuse: When are Restrictions on Capabilities Warranted?

arXiv 2303.09377 ↗First posted 16 Mar 2023cs.AI cs.CYcited by 15

Not on the map. Why?

Abstract (arXiv)

Artificial intelligence (AI) systems will increasingly be used to cause harm as they grow more capable. In fact, AI systems are already starting to be used to automate fraudulent activities, violate human rights, create harmful fake images, and identify dangerous toxins. To prevent some misuses of AI, we argue that targeted interventions on certain capabilities will be warranted. These restrictions may include controlling who can access certain types of AI models, what they can be used for, whether outputs are filtered or can be traced back to their user, and the resources needed to develop them. We also contend that some restrictions on non-AI capabilities needed to cause harm will be required. Though capability restrictions risk reducing use more than misuse (facing an unfavorable Misuse-Use Tradeoff), we argue that interventions on capabilities are warranted when other interventions are insufficient, the potential harm from misuse is high, and there are targeted ways to intervene on capabilities. We provide a taxonomy of interventions that can reduce AI misuse, focusing on the specific steps required for a misuse to cause harm (the Misuse Chain), and a framework to determine if an intervention is warranted. We apply this reasoning to three examples: predicting novel toxins, creating harmful images, and automating spear phishing campaigns.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 15 of its 138 references that are in the map. First posted on arXiv 16 Mar 2023.

Builds on

15 of 138 references.

References · 15

  1. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  2. 2021A General Language Assistant as a Laboratory for Alignmentcited by 1,023
  3. 2023A Watermark for Large Language Modelscited by 667
  4. 2022Predictability and Surprise in Large Generative Modelscited by 209
  5. 2020Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claimscited by 163
  6. 2018The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigationcited by 159
  7. 2023Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigationscited by 157
  8. 2020Responsible Disclosure of Generative Models Using Scalable Fingerprintingcited by 90
  9. 2023The Gradient of Generative AI Release: Methods and Considerationscited by 63
  10. 2022Structured access: an emerging paradigm for safe AI deploymentcited by 40
  11. 2021Institutionalising Ethics in AI through Broader Impact Requirementscited by 20
  12. 2022On the Horizon: Interactive and Compositional Deepfakescited by 13
3 more
  1. 2019The Offense-Defense Balance of Scientific Knowledge: Does Publishing AI Research Reduce Misuse?cited by 12
  2. 2021It's Time to Do Something: Mitigating the Negative Impacts of Computing Through a Change to the Peer Review Processcited by 11
  3. 2018Generative Models for Spear Phishing Posts on Social Mediacited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 3; 2025: 4; 2026: 2.

15 papers.

Sort:

By area: