Beyond Western Eyes: Rethinking AI Safety for Africa

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Author: Denis Avetisyan

Current AI safety evaluations often fail to account for the unique vulnerabilities and contextual risks present in African nations, demanding a more localized approach.

This review argues for the development of Africa-centric AI safety evaluations to address severe risks arising from infrastructural limitations, socio-political dynamics, and potential misalignment in AI systems.

Despite growing deployment of frontier AI systems across Africa, current safety evaluations largely fail to account for unique regional vulnerabilities and constraints. This paper, ‘Assessing the Case for Africa-Centric AI Safety Evaluations’, argues that neglecting these context-specific factors creates a portability gap, potentially leaving severe risks-defined here as harms resulting in mass casualties or economic losses exceeding 5% of a nation’s GDP-undetected. We develop a taxonomy linking potential harms to process pathways influenced by hazard, vulnerability, and exposure, alongside tailored threat modelling strategies for resource-limited settings. Given these challenges, can evaluations designed for Western contexts adequately anticipate-and mitigate-the distinct pathways to AI-related harm emerging across the African continent?

The Inevitable Harm: Defining the Stakes

Frontier Artificial Intelligence systems, representing the most advanced capabilities in the field, present genuine material risks that extend beyond theoretical concerns. These systems are not simply prone to error; they possess the potential to cause critical harm, encompassing loss of life, physical injury, and substantial economic damage. Unlike conventional software failures, the scale and speed at which these harms could manifest are unprecedented, driven by the systems’ increasing autonomy and complexity. The potential for widespread disruption isn’t limited to single events; cascading failures across interconnected systems, such as financial markets or critical infrastructure, represent a significant threat. Evaluating these risks requires acknowledging that even a relatively low probability of a catastrophic outcome, when multiplied by the sheer power of these technologies, translates into a non-negligible level of danger demanding proactive mitigation strategies.

The potential for severe harm from advanced artificial intelligence isn’t simply a matter of long-term, gradual decline, but stems from two critical characteristics: Suddenness and Amplification. Suddenness refers to the capacity of these systems to initiate harm at a pace that overwhelms existing response mechanisms – a critical infrastructure failure, a widespread disinformation campaign, or a rapid market crash could unfold in hours or even minutes. Simultaneously, Amplification describes how AI can exacerbate existing societal vulnerabilities; rather than creating entirely new threats, it can magnify the impact of existing weaknesses in political systems, economic structures, or even human cognitive biases. This combination – the speed of potential harm coupled with the scaling of existing problems – presents a unique and challenging risk profile demanding proactive mitigation strategies focused on resilience and adaptability.

Defining quantifiable thresholds for harm posed by advanced artificial intelligence is paramount, yet a universally applied standard fails to account for vast disparities in economic resilience. This research addresses this challenge by proposing a pragmatic benchmark: critical economic harm is defined as damage exceeding 5% of a nation’s Gross Domestic Product (GDP). This threshold isn’t intended as an absolute value, but rather as a relative measure acknowledging that a loss of this magnitude would represent a severe disruption for any economy, while also recognizing that the impact of such a loss will differ significantly depending on a country’s overall economic capacity. By anchoring the definition of harm to GDP, the framework facilitates a more nuanced and equitable assessment of AI-related risks across diverse global contexts, allowing for a more targeted approach to mitigation and governance.

Mapping the Failure Surface: A Framework for Risk Identification

A robust risk identification taxonomy requires a structured approach that connects defined outcome thresholds – quantifiable levels representing unacceptable consequences – to specific process pathways where those consequences might manifest. This taxonomy models risk not as a singular event, but as the convergence of three core components: hazard, the potential source of harm; vulnerability, the susceptibility of the system or asset to that harm; and exposure, the degree to which that system or asset is subject to the hazard. By explicitly defining these intersections, the taxonomy facilitates a systematic evaluation of potential risks, enabling prioritization based on the magnitude of consequence and the likelihood of occurrence within established process flows.

Employing established methodologies such as System-Theoretic Process Analysis (STPA) and Scenario Planning enhances the development of a risk taxonomy. STPA, a safety analysis technique, focuses on identifying systemic hazards arising from interactions within a complex system, allowing for proactive risk mitigation by analyzing control structures and potential failures. Scenario Planning complements this by developing multiple plausible future states, enabling the assessment of risk across a range of conditions and identifying vulnerabilities that may not be apparent under a single predicted outcome. The combined application of these methods provides a robust framework for systematically identifying and categorizing risks based on both system dynamics and potential future events.

Structured Expert Elicitation (SEE) systematically gathers and combines judgments from multiple experts to improve the accuracy and reliability of risk assessments. Unlike unstructured brainstorming, SEE employs predefined protocols-such as Delphi methods or aggregating individual probability estimates-to minimize cognitive biases and encourage independent thinking. Complementing SEE, Reference Class Forecasting (RCF) leverages historical data from analogous projects or events to establish realistic ranges for future risk outcomes. RCF recognizes that predicting specific values is often unreliable; instead, it focuses on identifying a relevant reference class-a group of similar past endeavors-and using its statistical properties to constrain predictions, thereby reducing overconfidence and improving forecast accuracy.

The Continent as Amplifier: Africa-Specific Risks and Data Needs

Africa-specific risks in AI deployment are heightened by existing infrastructural deficits and distinct societal dynamics. Limited access to reliable electricity and internet connectivity can compromise the functionality of AI systems, particularly those reliant on real-time data processing or cloud services. Furthermore, prevalent issues such as lower levels of digital literacy, linguistic diversity – requiring nuanced natural language processing – and the widespread informal economy create unique vulnerabilities to algorithmic bias and unintended consequences. These factors can amplify harms related to areas like healthcare access, financial inclusion, and agricultural productivity, necessitating tailored risk assessment and mitigation strategies beyond those developed for contexts with more robust infrastructure and established regulatory frameworks.

Effective AI safety evaluations in African contexts require substantial access to localized datasets due to the significant impact of environmental and societal factors on AI system performance. General datasets developed in other regions often lack the granularity and representation of conditions – such as infrastructure limitations, linguistic diversity, and unique socioeconomic indicators – prevalent across the African continent. This deficiency can lead to inaccurate model training, biased outputs, and ultimately, unreliable or harmful AI applications. Specifically, data reflecting local languages, geographic features, resource availability, and cultural norms are critical for building robust and safe AI systems. Without this ‘local data’, evaluations risk overlooking context-specific failure modes and failing to accurately predict real-world performance, increasing the potential for adverse outcomes.

Proactive AI safety evaluations, guided by a defined risk taxonomy, are crucial for identifying potential vulnerabilities prior to deployment in African contexts. These evaluations establish a threshold for “critical harm” defined as either the loss of thousands of lives or economic damage equivalent to 5% of a country’s Gross Domestic Product. This quantifiable metric allows for standardized assessment of AI system risks and facilitates prioritization of mitigation strategies based on the potential scale of negative impact, enabling developers and policymakers to address the most significant threats before they materialize.

The Illusion of Control: Toward Responsible AI: Policy and Mitigation

Artificial intelligence policy is increasingly recognized as fundamental to harnessing the benefits of this rapidly evolving technology while proactively addressing its inherent risks. These policies aren’t simply about regulation; they represent a crucial framework for guiding the development and deployment of AI systems in a manner that prioritizes societal well-being. Effective AI policy establishes ethical guidelines, promotes transparency in algorithmic decision-making, and fosters accountability for potential harms-ranging from bias and discrimination to privacy violations and economic disruption. By establishing clear standards and incentivizing responsible innovation, these policies aim to mitigate potential negative consequences and ensure that AI serves as a force for positive change, fostering trust and enabling widespread adoption across diverse sectors.

Effective AI policy necessitates a foundation built upon comprehensive risk assessments, moving beyond generalized concerns to pinpoint specific vulnerabilities within distinct applications and deployment contexts. A one-size-fits-all approach proves inadequate; the potential harms stemming from an AI-powered medical diagnosis tool, for instance, differ drastically from those presented by an AI used in financial trading or criminal justice. Therefore, policies must be meticulously tailored, acknowledging these nuanced risks and prioritizing mitigation strategies relevant to each specific scenario. This granular level of analysis allows for the development of targeted regulations and ethical guidelines, ensuring responsible innovation and minimizing unintended consequences as AI systems become increasingly integrated into daily life.

Long-term viability of artificial intelligence hinges on dedicated investment in comprehensive evaluation frameworks and diligent data collection efforts. These initiatives aren’t merely about technical performance, but also about proactively identifying and mitigating potential societal and economic risks. Researchers propose a pragmatic metric for defining ‘significant economic harm’ – a 5% threshold of a nation’s Gross Domestic Product – to focus resources on the most critical vulnerabilities. Such a benchmark allows for quantifiable risk assessment, guiding the development of AI systems that demonstrably contribute to societal benefit rather than posing substantial economic threats. Continuous monitoring and data analysis, informed by this threshold, are crucial for adapting to the evolving landscape of AI and ensuring its responsible deployment across all sectors.

The pursuit of universally applicable AI safety evaluations founders on the shoals of lived reality. This paper highlights how infrastructural disparities and socio-political contexts-particularly within Africa-create unique vulnerability profiles absent from standardized threat modelling. It echoes a sentiment articulated by Bertrand Russell: “The difficulty lies not so much in developing new ideas as in escaping from old ones.” The ingrained assumption of a globally homogenous risk landscape represents one such outdated idea. The study posits that safety isn’t a property of the AI, but a relationship between the system and its environment; a fractured infrastructure introduces cascading failure modes absent in more robust contexts. Attempts to impose solutions designed elsewhere risk amplifying existing dependencies, ensuring that when-not if-a failure occurs, the consequences are disproportionately severe.

What’s Next?

The call for Africa-centric AI safety evaluations isn’t a demand for better tooling, but an acknowledgement of inherent fragility. Current evaluations, built on assumptions of stable infrastructure and predictable social response, are prophecies of failure when transplanted. A system isn’t a machine, it’s a garden – and neglecting the soil of context will inevitably grow technical debt in the form of unforeseen consequences. The paper rightly points toward the need for localized threat modelling, but this isn’t simply about adding data points; it’s about embracing a fundamentally different epistemology.

The greatest challenge lies not in identifying specific vulnerabilities, but in accepting the inevitability of misalignment. Resilience lies not in isolation, but in forgiveness between components – the capacity for systems to degrade gracefully, to absorb shocks, and to continue functioning, even when faced with conditions they were not explicitly designed to handle. Future work must therefore move beyond preventative measures, towards adaptive architectures and robust recovery mechanisms.

The true test will be whether the field can resist the urge to build ‘safe’ systems, and instead cultivate ecosystems capable of learning from, and adapting to, the unpredictable currents of a complex world. The goal isn’t to eliminate risk, but to distribute it, to absorb it, and to build systems that can flourish even amidst uncertainty.

Original article: https://arxiv.org/pdf/2602.13757.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-17 08:53