Securing AGI Deployment and Mitigating Safety Risks

Jan 20, 2025

Securing AGI Deployment and Mitigating Safety Risks

Roshni Kumari

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Summary

As artificial general intelligence (AGI) systems near deployment readiness, they pose unprecedented challenges in ensuring safe, secure, and aligned operations. Without robust safety measures, AGI can pose significant risks, including misalignment with human values, malicious misuse, adversarial attacks, and data breaches.

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@misc {

title={

Securing AGI Deployment and Mitigating Safety Risks

author={

Roshni Kumari

date={

1/20/25

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Review

Reviewer Name

Constructive critique

Criteria 1:

Advancing interpretability (Possible questions to answer in this criteria: Does the project contribute to the field of mechanistic interpretability? Does it provide new insights into understanding or steering AI model behavior? How well does it move us towards reprogramming AI models? How original and innovative is the approach?)

2.5

Criteria 2:

Relevance for AI safety (Possible questions to answer in this criteria: How important is the contribution to advancing the field of AI safety? Do we expect the results to generalize beyond the specific case(s) presented in the submission? Does the approach introduce new safety mechanisms or enhance existing ones in innovative ways?))

2.5

Criteria 3:

Methodology & presentation (Possible questions to answer in this criteria: How well is the project executed from a technical standpoint?, Is the code well-structured, documented, and reproducible?, How effectively does it utilize Goodfire's SDK/API and other provided resources?, How clearly and effectively is the research presented in the paper?, Quality of visualizations and demos (if applicable), Clarity of methodology explanation and results interpretation))

2.5

Reviewer's Comments

Natalia Perez-Campanero

The proposal seems very ambitious. It does a good job of summarizing the safety risks of AGI, but is a little unclear on which of these the proposed solution would be targeting and how. To develop further, I would recommend focusing on one solution/use case and developing it further, thinking through implementation. Providing a clear definition of AGI would help in setting a foundation for a more concrete discussion of the safety risks and potential solutions/applications.

C1:

3.5

C2:

3.6

C3:

Shivam Raval

The paper describes a proposal for SentinelAI, a framework for securing AGI deployment and mitigating safety risks. The proposal identifies five different challenges and poses potential solutions that are based on techniques from existing literature. The proposal highlights an important need that would arise as autonomous systems become more and more capable, however, since it is based on 2027 AGI prediction, it might be realized in the near term. It might also be helpful if what an AGI system is comprised of, and some justification for how the existing approaches are going to be valid against a vastly capable system or how they can be modified to form the different components of SentinelAI. Some results using a simulated strong and capable advanced system (eg. o3) can be monitored using other strong or even less capable systems (a usual setup in oversight and control literature) can strengthen the proposal.

C1:

C2:

3.5

C3:

Pablo Sanzo

The paper does a good attempt at summarizing the different AI safety risks. It would have benefitted from proposing a definition for AGI, since the term is used broadly in the paper.

When it comes to the solution proposed, it is appreciated that several layers are explored, as to reduce the risks posed by AGI. The link to "Multi-Agent Simulation" seems broken (https://github.com/SentinelAI-MultiAgentSim).

I would have appreciated a deeper dive into how are frontier AI labs currently solving these challenges, and how the proposed solution can fit and be deployed within the industry (e.g. what incentives to use).

I encourage the team to continue working in this direction and, as soon as more results are available, to show a comparison between an "out-of-the-box" AI model, and one that has been risk-mitigated by SentinelAI.

C1:

3.5

C2:

C3:

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