Preventing catastrophic
and existential risks
from advanced AI.
Artificial intelligence has the potential to profoundly benefit humanity or to pose catastrophic risks to our survival. AI safety is the field dedicated to ensuring advanced AI systems remain beneficial, controllable, and aligned with human values.
What is existential
risk from AI?
Existential risk (x-risk) refers to threats that could cause human extinction or permanently and drastically curtail humanity's potential. Advanced AI systems, particularly artificial general intelligence (AGI) that matches or exceeds human capabilities across all domains, pose such risks if developed without adequate safety measures.
Leading AI researchers, including Geoffrey Hinton (Turing Award winner), Yoshua Bengio, and Stuart Russell, have warned that mitigating extinction risk from AI should be a global priority alongside pandemics and nuclear war.
The Alignment Problem
As AI systems become more capable, ensuring they pursue goals aligned with human values becomes exponentially harder. A superintelligent AI optimizing for the wrong objective could cause irreversible harm.
Loss of Control
Advanced AI systems may become too complex to understand or control. Once AI surpasses human intelligence in key domains, maintaining meaningful human oversight becomes a fundamental challenge.
Rapid Capability Gains
AI progress is accelerating. Systems that seemed impossible five years ago are now commonplace. This rapid advancement leaves little time to solve safety problems before deployment.
Deployment Without Safeguards
In regions with limited regulatory capacity, like much of Africa, advanced AI systems are deployed without adequate safety testing, creating conditions where catastrophic failures emerge first.
We have less time
than we think.
AI capabilities are advancing faster than safety research. GPT-2 (2019) was considered too dangerous to release. GPT-4 (2023) passes professional exams and writes production code. Current frontier models exhibit emergent capabilities their creators did not anticipate.
Many AI researchers estimate a 10-50% chance of AGI within the next 10-20 years. Even if these estimates are off by decades, the fundamental safety problems (alignment, interpretability, control) remain unsolved.
We cannot afford to wait until AGI is imminent to solve these problems. Safety research must happen now, while we still have time to get it right.
Why AI safety work in Zambia
matters globally.
Zambia is not peripheral to AI safety. It is central to it. The structural conditions here (infrastructure fragility, limited regulatory capacity, multilingual complexity) are precisely where AI safety failures surface first. Our work reveals vulnerabilities that affect AI deployment everywhere.
Infrastructure Fragility
AI systems designed for stable environments fail unpredictably when deployed in contexts with unreliable power, connectivity, and institutional capacity. These failure modes reveal safety gaps that affect everyone.
Institutional Weakness
Limited regulatory oversight means AI systems operate with minimal safety constraints. Zambia becomes a testing ground where alignment failures surface before global labs notice.
Multilingual Complexity
AI systems trained primarily on English fail to understand Bemba, Nyanja, Tonga, and other Zambian languages. This creates misalignment at the most basic level: the system cannot understand the people it affects.
Economic Vulnerability
When AI systems make errors in high-income countries, there are safety nets. In Zambia, a single algorithmic mistake in mobile money or agricultural lending can devastate livelihoods with no recourse.
From local harms
to global safety.
We address both near-term harms (deepfakes, fraud, algorithmic bias) and long-term existential risks (misalignment, loss of control, catastrophic deployment failures). These are not separate problems. They are connected.
Every voice-clone scam we document reveals alignment failures. Every deepfake election incident shows how AI systems can be misused at scale. Every algorithmic bias case demonstrates the challenge of value alignment across cultures.
By building AI safety infrastructure in Zambia (red teaming, harm monitoring, policy frameworks, research capacity) we contribute to reducing existential risk globally. Safe AI deployment in challenging contexts makes AI safer everywhere.
Help us build a safer AI future.
Whether you're a researcher, student, policymaker, or concerned citizen, there are ways to contribute to AI safety in Zambia and Africa.