Mar 10, 2025

BlueDot Impact Connect: A Comprehensive AI Safety Community Platform

Elise Racine, Aliya Koishina, Aishwarya Gurung, Tatenda Mawema, Haihao Liu

Track: Public Education

The AI safety field faces a critical challenge: while formal education resources are growing, personalized guidance and community connections remain scarce, especially for newcomers from diverse backgrounds. We propose BlueDot Impact Connect, a comprehensive AI Safety Community Platform designed to address this gap by creating a structured environment for knowledge transfer between experienced AI safety professionals and aspiring contributors, while fostering a vibrant community ecosystem. The platform will employ a sophisticated matching algorithm for mentorship that considers domain-specific expertise areas, career trajectories, and mentorship styles to create meaningful connections. Our solution features detailed AI safety-specific profiles, showcasing research publications, technical skills, specialized course completions, and research trajectories to facilitate optimal mentor-mentee pairings. The integrated community hub enables members to join specialized groups, participate in discussions, attend events, share resources, and connect with active members across the field. By implementing this platform with BlueDot Impact's community of 4,500+ professionals across 100+ countries, we anticipate significant improvements in mentee career trajectory clarity, research direction refinement, and community integration. We propose that by formalizing the mentorship process and creating robust community spaces, all accessible globally, this platform will help democratize access to AI safety expertise while creating a pipeline for expanding the field's talent pool—a crucial factor in addressing the complex challenge of catastrophic AI risk mitigation.

Reviewer's Comments

Reviewer's Comments

Arrow
Arrow
Arrow

This work shows a clear engagement with the problem, and a well-thought out approach to addressing key issues. Additional literature to support the choice of questions for interview design (for early testers) would further support this submission. I am also interested in hearing more about how the authors would navigate questions around user privacy and confidentiality (if desired), and which projects would be prioritized if the BlueDot Impact Connect team faced capacity constraints. For example, the mentor matching requires deep domain knowledge in order to appropriately match based on research interests and research contributions. Additionally, I am curious about the technical implementation of the solution, such as the matching algorithm (if done automatically) and/or the type of support provided to mentors to support them to deliver excellent mentoring with their limited time. As a public education project, the submission relies heavily on the strength of the mentor matching algorithm, and the skill of the mentors, and it would be great to see more information on these elements if the project develops further.

Amazing work! I'm also so happy to see the BlueDot community working together here!

Here are some things I particularly enjoyed:

- Beautiful and sleek UI!

- Spot on formulation of the problem with community and mentorship in the AI safety space. One other thing to explore is why have previous efforts of creating such a community not been sufficient. For example, there are several discord communities.

Here are some things I thought could be improved:

- I would have loved to learn more about how the mentors and mentees would be matched!

- Although the tiered mentorship model helps to alleviate some of the mentor bottleneck, I wonder if you considered other ways to incentivise mentors to participate. Similarly, it might be useful to chat with potential mentors to understand their challenges with being a mentor (e.g. time, worry about a bad match, poor past experience with mentorship).

I love these ideas and if I had to give one piece of advice, it would be to just pick one piece of this to work on further rather than try to tackle all 5 elements of the community hub AND connect at once. Build something that doesn't scale and test it out to see if it works!

Cite this work

@misc {

title={

BlueDot Impact Connect: A Comprehensive AI Safety Community Platform

},

author={

Elise Racine, Aliya Koishina, Aishwarya Gurung, Tatenda Mawema, Haihao Liu

},

date={

3/10/25

},

organization={Apart Research},

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

howpublished={https://apartresearch.com}

}

Recent Projects

View All

Feb 2, 2026

Markov Chain Lock Watermarking: Provably Secure Authentication for LLM Outputs

We present Markov Chain Lock (MCL) watermarking, a cryptographically secure framework for authenticating LLM outputs. MCL constrains token generation to follow a secret Markov chain over SHA-256 vocabulary partitions. Using doubly stochastic transition matrices, we prove four theoretical guarantees: (1) exponentially decaying false positive rates via Hoeffding bounds, (2) graceful degradation under adversarial modification with closed-form expected scores, (3) information-theoretic security without key access, and (4) bounded quality loss via KL divergence. Experiments on 173 Wikipedia prompts using Llama-3.2-3B demonstrate that the optimal 7-state soft cycle configuration achieves 100\% detection, 0\% FPR, and perplexity 4.20. Robustness testing confirms detection above 96\% even with 30\% word replacement. The framework enables $O(n)$ model-free detection, addressing EU AI Act Article 50 requirements. Code available at \url{https://github.com/ChenghengLi/MCLW}

Read More

Feb 2, 2026

Prototyping an Embedded Off-Switch for AI Compute

This project prototypes an embedded off-switch for AI accelerators. The security block requires periodic cryptographic authorization to operate: the chip generates a nonce, an external authority signs it, and the chip verifies the signature before granting time-limited permission. Without valid authorization, outputs are gated to zero. The design was implemented in HardCaml and validated in simulation.

Read More

Feb 2, 2026

Fingerprinting All AI Cluster I/O Without Mutually Trusted Processors

We design and simulate a "border patrol" device for generating cryptographic evidence of data traffic entering and leaving an AI cluster, while eliminating the specific analog and steganographic side-channels that post-hoc verification can not close. The device eliminates the need for any mutually trusted logic, while still meeting the security needs of the prover and verifier.

Read More

Feb 2, 2026

Markov Chain Lock Watermarking: Provably Secure Authentication for LLM Outputs

We present Markov Chain Lock (MCL) watermarking, a cryptographically secure framework for authenticating LLM outputs. MCL constrains token generation to follow a secret Markov chain over SHA-256 vocabulary partitions. Using doubly stochastic transition matrices, we prove four theoretical guarantees: (1) exponentially decaying false positive rates via Hoeffding bounds, (2) graceful degradation under adversarial modification with closed-form expected scores, (3) information-theoretic security without key access, and (4) bounded quality loss via KL divergence. Experiments on 173 Wikipedia prompts using Llama-3.2-3B demonstrate that the optimal 7-state soft cycle configuration achieves 100\% detection, 0\% FPR, and perplexity 4.20. Robustness testing confirms detection above 96\% even with 30\% word replacement. The framework enables $O(n)$ model-free detection, addressing EU AI Act Article 50 requirements. Code available at \url{https://github.com/ChenghengLi/MCLW}

Read More

Feb 2, 2026

Prototyping an Embedded Off-Switch for AI Compute

This project prototypes an embedded off-switch for AI accelerators. The security block requires periodic cryptographic authorization to operate: the chip generates a nonce, an external authority signs it, and the chip verifies the signature before granting time-limited permission. Without valid authorization, outputs are gated to zero. The design was implemented in HardCaml and validated in simulation.

Read More

This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.