Can Fragile Labs Use AI Safely in Liberia, West Africa?

Raising $10,000active

A small field test in Liberia to see whether resource-constrained public health laboratories can use AI tools safely before more powerful AI becomes routine in biological work.

Donate:Manifund

A small field test in Liberia to see whether resource-constrained public health laboratories can use AI tools safely before more powerful AI becomes routine in biological work.

Donate:Manifund

Project Details

This project builds on a pilot I already completed in Liberia, West Africa with seed support from Manifund.

In that pilot, I developed and tested the Biosafety Landscape Assessment Matrix (BLAM) . The purpose of BLAM was to move beyond general statements like “biosafety is weak” and identify specific gaps in real laboratory settings. I used it to look at issues such as incident reporting, pathogen accountability, waste management, equipment maintenance, staff training, and specimen transport. The pilot confirmed something important: Liberia has committed public health people and strong national leadership, but many parts of the laboratory system still have weak safety systems, especially outside the national level.

Now I want to test a new question: what happens when AI tools enter this kind of laboratory system?

Liberia relies heavily on the National Public Health Reference Laboratory (NPHRL) under National Public Health Institute of Liberia (NPHIL) which is executive arm of Ministry of Health in Liberia, West Africa. While county laboratories depend on national guidance and have fewer resources. If laboratory staff start using AI tools to draft procedures, summarize guidance, troubleshoot problems, or prepare reports, this could be useful. But it could also create risk if AI gives wrong advice, if sensitive laboratory information is entered into public tools, or if staff do not know how to verify AI-generated guidance.

I will work with the NPHRL and two connected county laboratories (I already have approvals secured based on my pilot work with them). I will first ask how staff are already using, or expect to use, AI. Then I will run a simple tabletop exercise using harmless fictional situations, such as an AI tool giving incorrect biosafety advice or a county laboratory receiving AI-generated guidance that conflicts with national instructions.

The concrete output will be a small practical toolkit:
an AI-biosecurity stress-test module added to BLAM
a short safe-use guide for AI in laboratory work
a checklist for verifying AI-generated technical advice
a simple reporting and escalation pathway for AI-related mistakes.

The point is not to write another academic paper. The point is to test whether a real, resource-constrained laboratory system can safely absorb AI before more powerful AI tools become routine in biological work. Facility-specific findings will remain confidential, but the general toolkit and lessons will be shared publicly so others can adapt them.

Theory of Impact

My theory of impact is that AI biosecurity will not only be decided in frontier labs or rich countries. As AI tools become cheaper and more capable, they will also enter ordinary public health laboratories in places where safety systems are already stretched.

Liberia, West Africa is a real example of this. In my earlier BLAM pilot, supported by seed funding from Manifund, I found that many biosafety gaps were not abstract. They were practical gaps: weak incident reporting, limited pathogen accountability, uneven training, equipment maintenance problems, and fragile links between the national laboratory and county laboratories.

If powerful AI tools enter this kind of system without safeguards, they may create new failure points. A well-intentioned lab worker could trust wrong AI advice, use AI to draft a procedure without checking it, enter sensitive laboratory information into a public tool, or fail to report an AI-related mistake because no reporting pathway exists.

This project reduces AI x-risk in a small but concrete way by testing these failure points before the technology becomes routine in biological work. I will use harmless fictional scenarios to see where staff get confused, where verification breaks down, and what kind of guidance is actually usable in a resource-constrained laboratory setting.

People

Saeed Ahmad

creator

Grants Received

Add grant

Manifund

from Manifund/ACX Grantsmanifund.org

$10,000

Funding Asks

grantmaking.ai Launch Round

Applied

Minimum

$10,000

Ideal

$20,000

How the money will be spent

I am asking for 20000 USD if possible but I could still run a smaller version of the project with 10000 USD.

With the minimum amount of 10000 USD, I would run a lean version of the field test. This would cover travel and local logistics in Liberia, one small tabletop exercise with the National Public Health Reference Laboratory and two county laboratories, support from a Liberian biosafety or laboratory professional for technical review, and my time to develop the AI-biosecurity stress-test module, safe-use guide, verification checklist, and escalation pathway.

With the ideal amount of 20000 USD, I would make the project more complete and useful. I would spend more time in Liberia, include three to four county laboratories instead of two, run a follow-up exercise after revising the toolkit, bring in external biosafety or AI-biosecurity review, and produce a cleaner public version of the toolkit for others to adapt.

A rough breakdown for the ideal amount would be:

5000 USD for Liberia field travel, transport, accommodation, and local logistics
3000 USD for tabletop exercises, meetings, materials, and participant-related costs
7000 USD for my protected time to design the project, work in Liberia, run the field test, analyze results, revise the toolkit, and write up the findings
3000 USD for external biosafety or AI-biosecurity review
2000 USD for documentation, toolkit design, communication, data security, and contingency

My PhD is currently unfunded, so protected time is a real constraint. This funding would allow me to actually do the work properly rather than treating it as a side activity.

I would not use the money to buy laboratory equipment or run a large workshop. The money would be used to test one clear idea: whether Liberia’s laboratory system can identify and manage AI-related biosafety risks before powerful AI becomes routine in biological work.

Discussion

SSaeed AhmadProgress update16d

[Progress update]

What progress have you made since your last update?

Since the last update, I have completed the BLAM Liberia seed pilot and produced the main project report. The project helped turn a broad problem - Liberia’s weak biosafety and biosecurity capacity identified in the 2023 JEE into a more practical assessment framework. I developed and piloted the Biosafety and Biosecurity Landscape Assessment Matrix (BLAM) to look at concrete laboratory-system gaps, including incident reporting, pathogen accountability, waste management, equipment maintenance, specimen referral, training, and governance.

The pilot confirmed that the problem is real, but also more nuanced than “lack of resources.” Liberia has strong public health leadership, committed laboratory staff, and useful institutional networks. The bigger challenge is converting those strengths into routine systems: clear standards, regular supervision, maintenance, reporting, and follow up.

The main output is now a BLAM scoping report and a clearer pathway for future work. The pilot also helped me refine the project direction. I now see BLAM not only as a biosafety assessment tool, but as a platform that can later be extended to test whether fragile laboratory systems are ready to use AI safely in biological work.

What are your next steps?

My next step is to use the pilot report to seek follow-on support for a focused scale-up phase. The immediate plan is to refine BLAM into a cleaner, easier-to-use tool with a scoring guide, indicators, and practical reporting templates. I also want to develop a small AI-biosecurity add-on module that asks a simple question: can public health laboratories safely use AI tools for tasks such as drafting SOPs, summarizing guidance, or preparing reports without exposing sensitive information or trusting incorrect advice?

The next phase would likely focus on the National Public Health Reference Laboratory in Monrovia and a small number of linked county laboratories. It would test BLAM more systematically, run a simple AI-biosecurity tabletop exercise, and produce practical outputs such as a safe-use guide, verification checklist, and escalation pathway for AI-related laboratory mistakes. Longer term, I want BLAM to become a Liberia-owned tool that can support better biosafety investment, partner coordination, and preparedness planning.

Is there anything others could help you with?

First, technical feedback from people with experience in biosafety, biosecurity, laboratory systems, or AI-biosecurity would be very useful. I especially need reviewers who can help make BLAM simpler, safer, and more credible before wider use.

Second, introductions to potential funders or collaborators interested in global health security, AI safety, pandemic preparedness, or laboratory strengthening would help move the work from seed pilot to scale up.

Third, I would appreciate practical support in turning the BLAM report into stronger public-facing materials: a short concept note, a clean tool/manual, and a funder-ready scale-up proposal. My PhD is currently unfunded, so support for protected time is also important if I am to keep pushing this work forward properly.