As the global community converges on London and Washington to debate the existential risks of frontier AI, a dangerous assumption has taken root: that AI safety is a universal constant.
The prevailing belief is that if a model is "aligned" in a lab in San Francisco or London, it is safe for the rest of the world. My experience as a Systems Engineer and AI governance practitioner in Nigeria suggests otherwise.
We are currently facing a "Socio-Technical Gap" that threatens to undermine global alignment efforts, leaving emerging markets as the "blind spots" of AI safety.
The Engineering Lens: From Chemical Plants to Neural Networks
My perspective is shaped by my background in Chemical Engineering. In industrial safety, we rely on the "Precautionary Principle."
When designing a chemical plant, we don’t assume a system is safe because it passed a simulation; we conduct rigorous Hazard and Operability (HAZOP) studies to identify site-specific failure modes.We understand that a system’s stability is inseparable from its environment.
AI safety currently lacks this industrial rigor. We are deploying frontier models, systems of immense complexity and potential volatility, without the "contextual pressure valves" necessary to ensure they remain aligned when they hit diverse, real-world data environments.
I view model auditing not as a bureaucratic hurdle, but as a critical engineering fail-safe.
The Problem: Safety Degradation and "Safety Dumping"
Current safety benchmarks, such as MMLU or TruthfulQA, are overwhelmingly Western-centric.
They test for bias, truthfulness, and refusal behaviors within a narrow cultural and linguistic corridor.
However, safety is not a static property of the weights and biases of a model; it is a dynamic interaction between the model and the user’s context.
Through my work on the Governly AI Policy Map, which tracks regulatory readiness across 54 nations, I have observed a phenomenon I call "Safety Degradation." In my preliminary analysis, I found that while 54 nations are rushing to adopt AI, fewer than 5% have the technical infrastructure required to verify the safety claims of the models they are importing.
When frontier models are prompted in regional dialects (such as Nigerian Pidgin) or within local socio-economic scenarios, standard RLHF filters often degrade.
This leads to "safety dumping," where models are deployed in non-Western markets with a fraction of the contextual testing required to ensure robustness. If a model is misaligned in Lagos, the global system is not safe.
A Proposal for Technical Governance: Contextual Red-Teaming
To bridge this gap, we must move beyond high-level policy and into verifiable technical governance. I propose the adoption of Mandatory Contextual Red-Teaming Reports (CRRs) for international AI deployment.
A CRR should not just be a narrative report; it must include quantitative benchmarks comparing refusal rates across different linguistic dialects and socio-economic prompts to identify specific failure modes.
By making these reports a prerequisite for a "License to Operate," governments can force companies to move from generic "System Cards" to verifiable, contextual alignment. This empowers "Middle Powers" to use their market access as a lever to force a race to the top in safety standards.
Conclusion: Toward Truly Global Alignment. If the goal of AI Safety is to prevent catastrophic misalignment, then that safety must be inclusive. We cannot claim to have solved the alignment problem if our models are only aligned with a fraction of the human population.
I seek to use the Pivotal Research Fellowship to transform these high-level frameworks into a technical "safety toolkit" that can be piloted within the UK’s research ecosystem.
By integrating Systems Engineering rigor with context-aware governance, we can ensure that frontier AI remains a tool for empowerment rather than a source of global instability.
