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Assessing the Case for Africa-Centric AI Safety Evaluations

Published 14 Feb 2026 in cs.CY | (2602.13757v1)

Abstract: Frontier AI systems are being adopted across Africa, yet most AI safety evaluations are designed and validated in Western environments. In this paper, we argue that the portability gap can leave Africa-centric pathways to severe harm untested when frontier AI systems are embedded in materially constrained and interdependent infrastructures. We define severe AI risks as material risks from frontier AI systems that result in critical harm, measured as the grave injury or death of thousands of people or economic loss and damage equivalent to five percent of a country's GDP. To support AI safety evaluation design, we develop a taxonomy for identifying Africa-centric severe AI risks. The taxonomy links outcome thresholds to process pathways that model risk as the intersection of hazard, vulnerability, and exposure. We distinguish severe risks by amplification and suddenness, where amplification requires that frontier AI be a necessary magnifier of latent danger and suddenness captures harms that materialise rapidly enough to overwhelm ordinary coping and governance capacity. We then propose threat modelling strategies for African contexts, surveying reference class forecasting, structured expert elicitation, scenario planning, and system theoretic process analysis, and tailoring them to constraints of limited resources, poor connectivity, limited technical expertise, weak state capacity, and conflict. We also examine AI misalignment risk, concluding that Africa is more likely to expose universal failure modes through distributional shift than to generate distinct pathways of misalignment. Finally, we offer practical guidance for running evaluations under resource constraints, emphasising open and extensible tooling, tiered evaluation pipelines, and sharing methods and findings to broaden evaluation scope.

Summary

  • The paper demonstrates that prevailing Western AI safety assessments fail to account for Africa’s unique infrastructural and sociotechnical constraints, highlighting a significant portability gap.
  • It introduces a formal taxonomy and adapted threat modeling techniques, integrating methods like RCF, SEE, and STPA tailored for resource-constrained environments.
  • The study offers actionable recommendations for developers, policymakers, and researchers to refine AI risk evaluations and safeguard against severe AI harms in African contexts.

Assessing the Case for Africa-Centric AI Safety Evaluations

Introduction

The paper "Assessing the Case for Africa-Centric AI Safety Evaluations" (2602.13757) offers a critical examination of the external validity of prevailing AI safety evaluation regimes relative to the infrastructural and sociotechnical realities of the African continent. The authors situate their analysis within the context of rapidly increasing deployment of frontier AI systems, noting the pronounced misalignment between the conditions assumed in leading evaluation frameworks—almost universally constructed around Western deployment environments—and the material, organizational, and systemic constraints characteristic of African settings. The paper's thesis is that this portability gap renders current evaluation paradigms incomplete, underspecifying and potentially missing Africa-centric pathways to severe AI harm.

Severe AI Risks and Portability Limitations in Evaluation Practices

The authors provide an exhaustive synthesis of the operational definitions of severe AI risk as adopted by leading AI developers (e.g., Anthropic, OpenAI, Google DeepMind), regulators (EU AI Act, California SB-53, New York RAISE Act), and catastrophic risk scholarship. The consensus metrics—measured primarily as grave injury or death of thousands, or economic loss exceeding 5% of a nation's GDP—form the empirical foundation for the Africa-centric taxonomy proposed in the paper.

A crucial contribution lies in the manuscript's problematization of the "framing trap" and the "portability trap" endemic to current safety evaluations. The framing trap is the practice of evaluating AI models in isolation, ignoring the sociotechnical and infrastructural conditions of deployment. The portability trap refers to the invalid assumption that findings from high-resource environments will generalize to materially constrained, infrastructure-interdependent African contexts. The authors argue convincingly that both effects bias the evaluation landscape, attenuate its coverage of critical threat vectors, and undermine the reliability of safety claims in globally representative deployments.

The analysis of African infrastructural realities—limited availability of reliable electricity, endemic low-resource clinical and governance systems, and highly interdependent critical infrastructure—foregrounds the structural amplifiers of risk. The application of universal risk thresholds (e.g., hundreds of billions of dollars in damage) in African contexts, the authors contend, is analytically indefensible, as economic impacts of much lower absolute value are existential in smaller economies.

Taxonomy and Process Pathways for Africa-Centric Severe AI Risks

The authors present a formal taxonomy for severe AI risks as operationalized for Africa. Outcome measures are anchored at two thresholds: (1) grave injury or death of thousands over months, or (2) hundreds over weeks, and economic loss pegged at 5% of country GDP. The process pathway model outlines the intersection of hazard, vulnerability, and exposure, drawing from disaster risk literature.

Two distinctive features—amplification and suddenness—are emphasized. Amplification requires that frontier AI acts as a necessary magnifier of latent risk not otherwise resulting in critical harm. Suddenness captures the capacity for risk to materialize faster than coping or governance mechanisms can respond, propelling otherwise manageable risks into severe territory. The framework is designed to be generative, enabling systematic reverse engineering from known or hypothetical harms to the role of AI amplification under local constraints, and is intended for adaptation by practitioners rather than as an exhaustive typology.

Threat Modelling Methodologies for African Contexts

The paper systematically surveys threat modelling techniques: reference class forecasting (RCF), structured expert elicitation (SEE), scenario planning, system theoretic process analysis (STPA), and hybrid threat modelling. Each method is evaluated and tailored for the typical resource, infrastructural, and governance challenges in African deployments.

  • Limited resources: Lightweight RCF and scenario planning are prioritized; resource-constrained environments are matched to iterative, cost-minimizing approaches.
  • Connectivity constraints: Scenario planning with focus on indirect propagation pathways (e.g., radio, in-person networks) is recommended.
  • Thin technical expertise: Hybrid SEE models leverage the complementarity of Western technical and African contextual experts.
  • Weak state capacity: STPA is highlighted for its ability to formally represent control failures in system governance, with modifications to account for variable state enforcement and regulatory coherence.
  • Conflict environments: Hybrid methods incorporating localized threat landscapes and conflict amplification factors are necessary for appropriately modelling heightened risks.

The contextual adaptation of these threat modelling techniques constitutes an actionable blueprint for practitioners, directly responding to the deficiencies of current, Western-centric evaluation regimes.

Investigation of Misalignment Risk: Universal or Context-Specific?

The manuscript addresses the hypothesis that Africa may instantiate distinct misalignment risk pathways. Decomposing the sources of misalignment—optimization mechanics, model architecture, and data distribution—the authors reject the notion of Africa-centric alignment failure mechanisms. Instead, they argue that the African context is more likely to operate as an exposure surface for universal failure modes (e.g., reward hacking, goal misgeneralization, distributional shift). Specific African contexts (limited local data, diverse language environments, governance gaps) are liable to foreground failures that remain latent in Western testing. The implication is that resolving African amplification of misalignment is both necessary for local safety and instrumental in stress-testing frontier AI for generalizable, cross-context robustness.

Practical Implementation of Africa-Centric Safety Evaluations

Extensive practical guidance is offered for researchers and institutions conducting evaluations under resource constraints. The paper advises prioritization strategies, such as tiered evaluation pipelines using lighter-weight or open models for most tasks, with escalation to frontier models only when warranted. Open and extensible frameworks (e.g., INSPECT, Control Arena) are recommended, though the authors stress the importance of realistic test environments reflecting on-the-ground deployment conditions (language code-switching, intermittent connectivity, system interdependence).

The paper details the prohibitive cost structures for both API-based and on-premise model evaluations, demonstrates the limitations of synthetic data for local context coverage, and identifies collaborative and pooled infrastructure as critical for expanding evaluation coverage. The tradeoff between contextual fidelity and evaluation latency is recognized as a persistent constraint in operational safety research, recommending pragmatic approaches that prioritize early detection of catastrophic risk within available means.

Recommendations for Developers, Policymakers, and Evaluators

The manuscript issues tailored recommendations:

  • Frontier AI developers must incorporate regionally-calibrated harm thresholds, expand evaluation to cover Africa-specific deployment conditions, and invest in African research capacity.
  • African policymakers are urged to make safety evaluations contextually stringent within national AI strategies, fund local evaluation infrastructure and expertise, and legislate support for local datasets.
  • Researchers should refine the taxonomy and methodologies proposed, treat local constraints as integral threat model variables, and adapt evaluation scope and timing to real-world risk reduction impact.

Theoretical and Practical Implications

The primary theoretical implication is the necessity of decentering the evaluation ecosystem from high-resource Western assumptions and treating infrastructural, linguistic, and governance-induced vulnerabilities as primary variables in AI safety research. Practically, the taxonomy and method adaptations provided by the paper furnish an immediately actionable playbook for expanding the domain of AI safety coverage to underrepresented, but increasingly high-stake, deployment environments.

The work signals that meaningful progress in global AI safety governance will depend on the representativeness of risk metrics and iterative adaptation of evaluation tools to domain-specific hazards, vulnerabilities, and exposures. Furthermore, as the African context surfaces failure modes latent elsewhere, increased investment in Africa-centric safety research could have outsized benefits for the global AI safety community.

Conclusion

"Assessing the Case for Africa-Centric AI Safety Evaluations" (2602.13757) advances the discourse on frontier AI safety evaluation by foregrounding the non-portability of Western-designed assessments to African deployment environments. Through a technically rigorous taxonomy, context-adapted threat models, and detailed practical guidance, the paper creates a foundation for globalizing the scope of AI risk evaluation. This paradigm shift is essential to avoiding the systematic underestimation of catastrophic AI risks in the Global South and to ensuring that the safety and trustworthiness of AI systems is calibrated against the full spectrum of deployment realities.

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Overview: What is this paper about?

This paper looks at how powerful AI systems are being used in Africa, and asks whether current “AI safety tests” are good enough for African realities. The authors argue that most safety checks were designed in wealthy countries with strong infrastructure, and may miss serious dangers that can happen in places with fewer resources and more interconnected systems. They propose a way to spot Africa-specific severe AI risks and suggest practical methods to test AI safely in these contexts.

What questions does the paper try to answer?

The paper tries to answer three big, easy-to-understand questions:

  • Which AI-related dangers could cause truly serious harm in African settings?
  • Why might safety tests built for Western environments fail to catch those dangers?
  • How can governments, researchers, and companies build better tests that fit Africa’s real-world conditions?

How did the authors study the problem?

The authors build a clear framework (a structured way of thinking) and review methods for “threat modeling,” which is like brainstorming and mapping out how things could go wrong before they do.

Here are the key ideas and simple analogies they use:

  • Severe AI risk: They define “severe” harm as either the grave injury or death of thousands of people, or big economic damage equal to 5% of a country’s yearly economy. Think of it like this: if you lost 5% of your family’s income, it would be tough but recoverable; if you lost 150%, that’s impossible. Using a percentage makes the threshold fair for countries with different sizes of economies.
  • Hazard, vulnerability, exposure: This is a classic risk triangle.
    • Hazard is the danger itself (like a lit match).
    • Vulnerability is the weakness that lets the danger cause harm (dry leaves lying around).
    • Exposure is how much is at risk and where the danger can reach (a whole yard covered in dry leaves).
    • Together, they show how a small spark can become a wildfire if the conditions are right.
  • Amplification: Frontier AI acts like a megaphone or turbocharger. It can make existing problems much worse, faster, and at larger scale. The paper says a “severe AI risk” should only count if the harm wouldn’t have happened without the AI’s involvement.
  • Suddenness: Timing matters. Harm that hits quickly can overwhelm normal coping systems. For example, a flood arriving in hours causes more chaos than slow rising water over months. The paper focuses on fast, high-impact harms.
  • The “portability problem”: Tests done in one setting don’t always “fit” another. Imagine buying shoes that were sized using a different country’s system—they might not fit your feet. If AI safety tests assume perfect internet, lots of trained staff, or stable electricity, they may not predict what happens in places where those things are limited.
  • Threat modeling techniques: The authors review practical strategies and adapt them for African contexts:
    • Reference class forecasting: Look at similar past events to predict outcomes (like studying past blackouts to guess how a new AI power tool could fail).
    • Structured expert elicitation: Ask specialists to estimate risks when data is missing, and combine their answers carefully (include local experts who understand the ground reality).
    • Scenario planning: Build detailed “what if” stories of future events to test how systems would react.
    • System Theoretic Process Analysis (STPA): Examine how complex systems can fail because of hidden interactions, not just single parts breaking.

They tailor these methods to common constraints in Africa: limited budgets, patchy internet, fewer AI specialists, weaker state capacity, and sometimes conflict.

What did they find, and why does it matter?

Here are the main takeaways, explained simply:

  • Current AI safety tests may miss Africa-specific pathways to severe harm. Because many African systems—health, electricity, water, transport—are more interconnected and often less reliable, small AI mistakes can spread and grow into big problems fast.
  • A new taxonomy helps spot real severe risks. The paper ties clear harm thresholds (deaths, injuries, or 5% GDP loss) to the process pathways (hazard, vulnerability, exposure), plus two key features: AI amplification and suddenness. This makes it easier to identify which risks are truly severe in context.
  • Threat modeling must include local realities. Including local experts and data, and adjusting methods to fit resource limits, improves the odds that tests will catch hidden dangers.
  • Misalignment risks are mostly universal. The authors argue Africa probably won’t create brand-new types of misaligned AI behavior. Instead, Africa is more likely to reveal “universal failure modes” (problems any model could have) because the data, languages, and conditions are different from what models were trained on. This is called “distributional shift” (think: teaching a robot in a calm classroom, then expecting it to perform well in a noisy marketplace).
  • Practical guidance under resource constraints. They suggest using open-source tools, setting up tiered test pipelines (start simple, add complexity), and sharing methods and results widely so others can build on them.

These findings matter because AI is already being piloted in critical areas in Africa, like healthcare. If tests don’t reflect local conditions, “safe” systems could still cause serious harm.

What could be the impact?

If governments, researchers, and AI companies use the paper’s approach:

  • AI deployments in Africa can be made safer and more reliable, reducing the chances of sudden, large-scale harm.
  • Safety evaluations will become more fair and globally representative, improving trust and performance everywhere.
  • The broader AI community can learn from Africa’s diverse conditions, helping models handle different languages, infrastructures, and social systems better.
  • Policymakers can set smarter rules that require context-aware testing before AI is used in critical services.

In short

This paper makes a strong case that AI safety tests need to fit the places where AI will be used. By defining what “severe” harm looks like, explaining how risks grow in real-world systems, and adapting threat modeling techniques to African realities, the authors show practical ways to catch dangers before they happen. Doing this will help protect lives and economies, not just in Africa, but everywhere AI is deployed.

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Knowledge Gaps

Knowledge gaps, limitations, and open questions

Below is a single, focused list of what remains missing, uncertain, or unexplored, framed to be actionable for future research.

  • Empirical validation of the proposed taxonomy: no case studies, simulations, or retrospective analyses showing that the hazard–vulnerability–exposure framework correctly predicts Africa-centric severe AI risk pathways.
  • Operationalizing the “5% of GDP” economic harm threshold: unclear methods for estimating losses in low-data environments, handling inflation/PPP/base-year choice, and attributing indirect and informal-sector impacts.
  • Causal attribution of AI “amplification”: no methodology for establishing but-for causality (e.g., counterfactual models, audit trails, causal inference designs) to determine when frontier AI is a necessary magnifier of harm.
  • Measuring “suddenness”: lack of concrete temporal metrics (time windows, detection triggers) and statistical indicators to distinguish rapid-onset harms from gradual degradation in African settings.
  • Concrete catalog of Africa-centric severe AI scenarios: missing sector-specific attack trees and evaluation tasks for healthcare, power grids, water systems, agriculture, transportation, elections/disinformation, cybercrime, and biosecurity.
  • Infrastructure interdependency modeling: absence of network-based models and data to quantify cascade risks across cyber–physical–organizational layers in African cities and regions.
  • Benchmark datasets: no standardized, public evaluation datasets for low-resource African languages, dialects, code-switching, creoles, and local domain knowledge to assess capability/propensity/control.
  • Validation of tailored threat-modelling techniques: no comparative studies showing that the adapted RCF, SEE, scenario planning, and STPA variants outperform standard methods under constraints (limited resources/connectivity/expertise).
  • Structured expert elicitation design: unresolved questions about expert selection, calibration scoring, weighting of local vs. external expertise, bias mitigation, and compensation/incentives in African contexts.
  • Unknown-unknown discovery methods: missing protocols for stress-testing models beyond known scenarios (e.g., fuzzing, randomized exploration, Monte Carlo scenario generators, adversarial environment simulation).
  • Red-teaming under resource constraints: lack of practical red-team playbooks for offline/low-connectivity settings, physical security risks, and community-based adversarial testing.
  • Post-deployment monitoring in weak institutions: unclear designs for incident reporting, early-warning systems, safety telemetry, and feedback loops that work with limited capacity and conflict conditions.
  • Practical evaluation pipelines: no concrete pass/fail criteria, risk scoring rubrics, reproducibility requirements, resource budgets, and toolchains to implement “tiered evaluation” in low-resource environments.
  • Tooling specificity: insufficient detail on open-source tools, test harnesses, sandboxes, and edge/offline inference setups suitable for constrained infrastructure.
  • Data collection governance: lack of guidance on consent, privacy, data sovereignty, and ethical review processes (IRB-like) for assembling Africa-relevant evaluation datasets.
  • Economic loss attribution in informal economies: no methodology to capture household welfare shocks, microenterprise impacts, and supply-chain disruptions where official statistics are sparse.
  • Heterogeneity across African contexts: limited guidance on adapting evaluations to cross-country and urban–rural differences, conflict vs. post-conflict zones, and varying levels of state capacity.
  • Integration with African policy/regulation: missing map of national/regional regulatory requirements, alignment pathways with the AU Continental AI Strategy, and mechanisms to embed evaluations into procurement and oversight.
  • Incentives for frontier developers: no concrete policy levers (standards, procurement clauses, accreditation, funding conditions) to compel inclusion of Africa-centric evaluations in release decisions.
  • Balancing safety and beneficial deployment: no framework for benefit–risk trade-offs to avoid undue delays in life-improving AI applications while meeting severe-risk thresholds.
  • Misalignment risk claims: the assertion that Africa mainly exposes universal failure modes via distributional shift lacks empirical testing; no benchmarks to measure deception, situational awareness, or sandbagging in African contexts.
  • Potential for distinct misalignment pathways: unexplored whether language diversity, cultural norms, or atypical operational environments could yield novel misalignment modes unique to African deployments.
  • Connectivity/power instability tests: missing evaluations for model behavior under intermittent power, network dropouts, context resets, and synchronization failures typical in African infrastructures.
  • Attacker models specific to Africa: insufficient characterization of threat actors (organized crime, armed groups, political operatives), their capabilities, and realistic misuse pathways of frontier AI.
  • Evaluation of non-frontier, widely deployed AI: focus on frontier models leaves gaps for commonly used non-frontier systems (mobile apps, decision support tools) that may still create severe harms in constrained settings.
  • Sharing findings safely: no disclosure policy framework balancing openness (to improve evaluations) with misuse risk when publishing vulnerabilities relevant to critical infrastructures.
  • Cross-region portability: unclear how Africa-centric evaluation methods generalize to other Global South contexts; need external validity studies across diverse low-resource environments.
  • Capacity-building roadmap: no concrete plan for training, institutional partnerships, funding mechanisms, and modular curricula to scale Africa-centric evaluation capabilities.
  • Digital twin testbeds: absence of synthetic “African infrastructure” simulators/digital twins to safely test cascade and amplification scenarios before real-world deployment.
  • Sector-specific safeguards: limited practical guidance for integrating evaluations into healthcare triage systems, public health emergency operations, utility SCADA/ICS protections, and election oversight mechanisms.
  • Measurement of sociotechnical factors: missing instruments to quantify governance fragility, corruption, chain-of-command failures, and human factors that mediate AI-enabled harms.
  • Evaluation of multilingual prompts and toxicity: no standardized procedures to assess manipulation, misinformation, and harmful outputs across code-switching, transliteration, and mixed-script scenarios common in African communication.
  • Resource requirements and cost models: lack of transparent estimates of the time, personnel, compute, and financial costs needed to execute proposed evaluation pipelines in different African contexts.
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Practical Applications

Immediate Applications

The following items can be deployed now, drawing directly on the paper’s taxonomy, threat-modeling guidance, and practical advice for running evaluations under resource constraints.

  • Industry (software/AI developers): Portability-aware safety evaluations for African deployments
    • Use case: Add a “portability gate” to release reviews that requires modeling hazard–vulnerability–exposure for target African markets and sectors.
    • Workflow/tools: STPA worksheets; scenario-planning templates; reference-class forecasting (RCF) datasets; localized red-team playbooks; tiered evaluation pipelines.
    • Sectors: Software, healthcare, finance, energy, telecom.
    • Dependencies/assumptions: Access to local partners and domain expertise; product team capacity; model-provider cooperation (eval APIs, logging).
  • Industry (healthcare tech): Tiered evaluation pipeline for clinic deployments under constrained infrastructure
    • Use case: Pre-deployment safety checks for AI triage/decision-support in clinics with intermittent power, limited staff, and poor connectivity.
    • Workflow/tools: Hybrid structured expert elicitation (SEE) panels with African clinicians/public health officials; offline evaluation harnesses; checklists for fail-safes and human-in-the-loop defaults; localized language prompts.
    • Sector: Healthcare.
    • Dependencies/assumptions: Ministry approvals; clinic IT buy-in; medical liability coverage; access to representative local data.
  • Industry (telecom/energy): Stress tests for AI-enabled operations under poor connectivity and cascading failures
    • Use case: Test fault tolerance of AI routing/maintenance systems where outages propagate across interdependent infrastructures.
    • Workflow/tools: STPA of cross-infrastructure interdependencies; cascading-failure scenarios; risk dashboards with escalation thresholds; “safe mode” policies.
    • Sectors: Energy, telecom, logistics.
    • Dependencies/assumptions: Historical incident data; cross-operator coordination; sensor and telemetry access.
  • Academia: Build an Africa-centric reference-class archive for severe risk forecasting
    • Use case: Open dataset of historical mass-casualty/economic-shock events annotated by hazard–vulnerability–exposure for use in RCF and scenario design.
    • Workflow/tools: Data curation protocols; transparent coding rubrics; open licensing; simple CSV/JSON artifacts for low-compute environments.
    • Sectors: Cross-sector research; public policy.
    • Dependencies/assumptions: Ethics approvals; funding for archival work; cooperation from local institutions.
  • Academia + Industry: Run hybrid SEE panels that combine frontier-AI capability experts with African domain experts
    • Use case: Rapid probabilistic estimates of amplification and suddenness risks for sector-specific deployments (e.g., biosecurity, mobile money).
    • Workflow/tools: Delphi protocols; calibration scoring; structured uncertainty quantification; published Elicitation Reports.
    • Sectors: Healthcare, biosecurity, finance, education, governance.
    • Dependencies/assumptions: Expert recruitment diversity; facilitation capacity; availability of “test questions” for calibration.
  • Policy: Context-aware procurement requirements and safety thresholds
    • Use case: Require vendors to submit Africa-centric safety evaluations using the paper’s thresholds (thousands of deaths or ≥5% GDP loss) and process pathways.
    • Workflow/tools: Standardized tender clauses; compliance checklists; independent audit pathways.
    • Sectors: Public sector procurement across health, education, energy, telecom.
    • Dependencies/assumptions: Legislative or regulatory authority; oversight capacity; auditor independence.
  • Policy + Industry: Low-bandwidth incident reporting and post-market monitoring networks
    • Use case: National or provincial WhatsApp/SMS lines and webforms for AI-related harm or near-miss reports, integrated with dashboards and escalation protocols.
    • Workflow/tools: Incident taxonomy (amplification and suddenness tags); privacy-preserving intake; triage playbooks; feedback to developers.
    • Sectors: Healthcare, finance, education, civil society.
    • Dependencies/assumptions: Data protection; public awareness; interagency coordination; response SLAs.
  • Daily life (clinics/NGOs/schools): Safety checklists and guardrails for AI-assisted decisions
    • Use case: Practical prompts and “stop/verify/translate” checklists to reduce misinterpretation and prevent overreliance on AI outputs in local languages.
    • Workflow/tools: Printable checklists; laminated decision-trees; offline fallback protocols; quick reference cards for escalation to human experts.
    • Sectors: Healthcare, education, social services.
    • Dependencies/assumptions: Staff training; translation quality; organizational buy-in.
  • Industry + Academia: Open and extensible low-compute evaluation toolkits
    • Use case: Lightweight harnesses that run CSV prompts, scoring rubrics, and scenario suites in low-resource environments; localized language test sets.
    • Workflow/tools: Python notebooks; CLI tools; simple logging; shared repositories; “sector packs” (health, energy, finance).
    • Sectors: Software, education, healthcare, energy.
    • Dependencies/assumptions: Open licensing; community maintenance; translation pipeline.
  • Policy + Industry: “Distributional Shift Assurance” before deployment
    • Use case: Mandatory tests demonstrating performance on local-language data and region-specific conditions; capability restrictions in high-criticality settings.
    • Workflow/tools: Shift-detection metrics; localized eval sets; model-carding sections on portability; staged rollout protocols.
    • Sectors: Healthcare, finance, education, public services.
    • Dependencies/assumptions: Access to local datasets; model configuration controls; governance willingness to delay launches.
  • Finance (mobile money/lending): AI risk controls for manipulation and misinformation
    • Use case: Evaluate LLM-enabled customer support and credit-scoring systems for exploitability, hallucinations, and fraudulent amplification.
    • Workflow/tools: Anomaly detection plus human review; adversarial prompt libraries in local languages; customer safety messaging.
    • Sector: Finance/fintech.
    • Dependencies/assumptions: Data-sharing agreements; fraud-monitoring infrastructure; regulatory guidance.
  • Education: Curriculum modules on Africa-centric AI safety and evaluations
    • Use case: Integrate the paper’s taxonomy, STPA labs, and case-based scenario planning into undergraduate and vocational courses.
    • Workflow/tools: Teaching packs; case studies; simple simulation exercises; assessment rubrics.
    • Sector: Education.
    • Dependencies/assumptions: Faculty capacity; curricular approvals; open educational resources.

Long-Term Applications

These items require further research, scaling, development, or institutional capacity building before broad deployment.

  • Healthcare: Africa-centric medical AI benchmarks and datasets
    • Use case: Multilingual, epidemiology-aware benchmark suites for clinical LLMs and decision-support tools validated in low-resource contexts.
    • Workflow/tools: Federated data infrastructure; ethics-by-design pipelines; post-market surveillance hooks.
    • Dependencies/assumptions: Robust health data governance; sustained funding; privacy-preserving technical stacks.
  • Cross-sector: Simulation platforms for interdependent infrastructures with AI agents
    • Use case: Digital twins to test cascading failure scenarios across power, water, telecom, transport with AI-in-the-loop control and safe-mode fallbacks.
    • Workflow/tools: Open-source simulators; shared scenario libraries; standardized metrics (amplification, suddenness).
    • Dependencies/assumptions: High-quality infrastructure data; compute resources; multi-agency participation.
  • Policy/institutions: Regional AI safety institutes and certification
    • Use case: AU or subregional centers standardize context-aware evaluations, certify models for African deployment, and maintain incident repositories.
    • Workflow/tools: Accreditation frameworks; audit protocols; model registries; public dashboards.
    • Dependencies/assumptions: Political will; sustained budgets; cooperation from developers.
  • Industry (MLOps): Productized “Contextual Safety Gate” for ML pipelines
    • Use case: SaaS tooling integrated into CI/CD that automates localized threat modeling, risk scoring, and scenario-based tests before release in target locales.
    • Workflow/tools: APIs to run STPA/RCF/SEE workflows; localization plugins; governance connectors (tickets, sign-offs).
    • Dependencies/assumptions: Market demand; access to model internals/logs; compliance integration.
  • Energy/Telecom: Cross-infrastructure early warning systems for AI-driven anomalies
    • Use case: Real-time monitoring that detects unsafe AI behavior or distributional shifts and triggers automated safe-mode across interdependent systems.
    • Workflow/tools: Unified telemetry; anomaly models; runbooks; failover designs.
    • Dependencies/assumptions: Sensor networks; interoperability standards; incident response teams.
  • Academia: Longitudinal studies of misalignment via distributional shift
    • Use case: Empirical programs to uncover universal failure modes revealed by African context shifts; contribute to global benchmarks and mitigations.
    • Workflow/tools: Shared testbeds; access to frontier models; cross-institution consortia.
    • Dependencies/assumptions: Legal access to models; research funding; data-sharing agreements.
  • Policy: Legal frameworks mandating portability checks and staged releases for critical sectors
    • Use case: Harmonize with EU AI Act principles while embedding Africa-specific thresholds and process-pathway requirements.
    • Workflow/tools: Statutes/regulations; compliance toolkits; independent audit capacity.
    • Dependencies/assumptions: Legislative timelines; enforcement readiness; regulator expertise.
  • Daily life/civil society: Community-led AI risk literacy and reporting programs
    • Use case: Sustained public education to recognize AI-generated misinformation and report severe-risk indicators in local languages.
    • Workflow/tools: Radio/TV/social campaigns; school modules; NGO-run hotlines.
    • Dependencies/assumptions: NGO capacity; donor support; trust in institutions.
  • Robotics/logistics: Conflict- and low-connectivity-sensitive protocols for drones and autonomous systems
    • Use case: Safe deployment patterns for medical supply drones or agricultural robotics with constraints-aware behavior and emergency overrides.
    • Workflow/tools: Contextual STPA; geo-fencing; mission abort criteria; human authorization loops.
    • Dependencies/assumptions: Aviation regulations; secure comms; operator training.
  • Finance: Distributional-shift audited AI credit scoring and customer service
    • Use case: Ensure fairness and robustness for mobile lending models across diverse local demographics and linguistic contexts.
    • Workflow/tools: Shift metrics; fairness audits; post-market monitoring; capability throttles.
    • Dependencies/assumptions: Regulator collaboration; standardized audit protocols; dataset representativeness.
  • Education: National repositories of local-language corpora and safety eval sets
    • Use case: Open corpora and evaluation suites to improve model portability and safety for African languages and dialects.
    • Workflow/tools: Community collection; annotation platforms; licensing frameworks.
    • Dependencies/assumptions: IP clarity; contributor incentives; sustained maintenance.
  • Software/healthcare: Off-grid evaluation and serving environments
    • Use case: Edge devices and offline model proxies for clinics to reduce reliance on cloud connectivity while preserving safety checks and audit trails.
    • Workflow/tools: Lightweight inference stacks; secure update channels; local logging and sync.
    • Dependencies/assumptions: Hardware procurement; energy reliability; secure distribution.

These applications translate the paper’s contributions—its severe-risk taxonomy (outcomes, process pathways, amplification, suddenness), Africa-tailored threat-modeling strategies (RCF, SEE, scenario planning, STPA), and practical guidance on open, extensible tooling and tiered pipelines—into concrete steps across industry, academia, policy, and daily practice. Each item’s viability depends on local partnerships, access to data and models, governance capacity, and sustained funding.

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Glossary

  • Amplification: A mechanism by which frontier AI magnifies latent dangers so that harms exceed what would occur otherwise. "We distinguish severe risks by amplification and suddenness, where amplification requires that frontier AI be a necessary magnifier of latent danger"
  • Asset-centric models: Threat modelling approach that prioritizes protecting specific resources or assets. "Asset-centric models focus on the resources or assets that need protection."
  • Autonomous AI Research and Development (AI R&D): Risk category involving AI systems that can improve or replicate their own capabilities with limited human oversight. "Anthropic's Responsible Scaling Policy identifies and evaluates two severe AI risks they consider the 'most pressing catastrophic risks': chemical, biological, radiological and nuclear (CBRN) risks and Autonomous AI Research and Development (AI R&D)."
  • But-for test: A causal standard asking whether harm would have occurred without a specific factor; used here to assess AI’s role as a necessary cause. "This draws from the 'but-for' test, a causal standard often used to determine liability"
  • Capability evaluations: Tests measuring what a model can do to inform its risk profile. "Capability evaluations measure what a model is able to do and how those abilities inform its potential risk profile"
  • Catastrophic risk: Extremely large-scale harms that disrupt society, often involving mass casualties or massive economic damage. "The ways in which AI systems may contribute to catastrophic risk have been classified into four categories"
  • Chemical, biological, radiological and nuclear (CBRN) risks: Severe threat domain where AI could enable or amplify dangerous capabilities related to CBRN hazards. "Anthropic's Responsible Scaling Policy identifies and evaluates two severe AI risks they consider the 'most pressing catastrophic risks': chemical, biological, radiological and nuclear (CBRN) risks and Autonomous AI Research and Development (AI R&D)."
  • Combinatorial complexity: The explosion of possible interactions among factors, making it hard to anticipate dangerous emergent patterns. "Likewise, combinatorial complexity can mean that one or more unaccounted-for factors combine with accounted-for factors to create an 'unknown unknown' scenario."
  • Contextual safety evaluations: Tests assessing how models affect real-world outcomes within their deployment context. "Ji et al. distinguish between model safety evaluations, which evaluate the outputs of models alone, and contextual safety evaluations, which evaluate how models impact real-world outcomes such as user behaviour and decision-making."
  • Control evaluations: Tests of whether safety mechanisms hold when models attempt to bypass them. "Control evaluations assess whether safety protocols remain effective when models intentionally try to override them."
  • Critical infrastructure: Systems, facilities, and assets essential for societal and economic functioning. "By critical infrastructure, we refer to systems, facilities, and assets essential to the functioning of society and the economy."
  • Distributional shift: A change between training and deployment data distributions that can expose model failure modes. "Africa is more likely to expose universal failure modes through distributional shift than to generate distinct pathways of misalignment."
  • External criticality: Importance arising from interdependencies, where one system’s failure propagates across others. "By contrast, external criticality arises from interdependencies between infrastructures, where failure in one system propagates disruption across others."
  • External validity: The extent to which evaluation results generalize beyond the original testing context. "Evaluations are often designed within specific contexts and assumed to apply beyond them, yet they have been faulted for lacking external validity by failing to generalise beyond their testing context."
  • Exposure: The extent to which people or systems come into contact with a hazard given existing vulnerabilities. "Process pathways refer to the complex intersection of vulnerability, hazard, and exposure"
  • Framing trap: Evaluating AI in isolation from the sociotechnical system, missing context-specific risks. "This has been described as the 'framing trap', where evaluations test algorithmic outputs without modelling the sociotechnical system in which the AI operates"
  • Foundation models: Large-scale pretrained models that can be adapted for many downstream tasks. "frontier AI systems are highly capable foundation models that may possess dangerous capabilities sufficient to pose severe risks to public safety"
  • Frontier AI: Highly capable foundation models whose dangerous capabilities can pose severe public safety risks. "frontier AI systems are highly capable foundation models that may possess dangerous capabilities sufficient to pose severe risks to public safety"
  • Frontier AI Safety Commitments: Voluntary developer commitments focused on severe risks from advanced AI. "This approach is consistent with the Frontier AI Safety Commitments made at the AI Seoul Summit in 2024"
  • Hazard: The source of danger in a risk pathway. "A hazard is the source of danger"
  • Inherent criticality: Importance due to direct societal or safety impacts if disrupted (e.g., electricity, water, emergency care). "Inherent criticality refers to infrastructures whose disruption would directly result in societal, economic, or safety consequences such as electricity supply, water systems or emergency healthcare services."
  • Interdependence: Coupling across infrastructures that allows failures to cascade among systems. "Research demonstrates that infrastructure interdependence means failures affecting a single infrastructure can cascade across systems."
  • Machine learning research and development (ML R&D): Work that advances ML capabilities; in safety, a risk vector where AI may accelerate ML progress. "machine learning research and development (ML R&D) risks could prove severe."
  • Misalignment risk: Risk that AI system goals or behaviors diverge from intended human objectives, leading to harm. "Its approach considers severe risk with reference to the capability level at which misuse, misalignment, and machine learning research and development (ML R&D) risks could prove severe."
  • Model safety evaluations: Tests focused on model outputs in isolation from deployment context. "Ji et al. distinguish between model safety evaluations, which evaluate the outputs of models alone, and contextual safety evaluations"
  • Optimism bias: Tendency to underrate risks and expect best-case outcomes. "This helps counter optimism bias-the tendency to expect best-case outcomes while ignoring failure rates."
  • Outside view: Forecasting perspective using base rates from comparable cases rather than case-specific details. "Reference Class Forecasting (RCF) predicts outcomes by examining what typically happened in similar past situations, an outside view based on historical patterns rather than focusing on Al's unique features."
  • Portability trap: Assuming solutions or evaluations transfer across contexts when they may become harmful elsewhere. "The framing trap is compounded by the 'portability trap', where solutions evaluated in one social context may be harmful or misleading when applied elsewhere."
  • Preparedness Framework: OpenAI’s framework for tracking and preparing for capabilities that create severe risks. "OpenAI's Preparedness Framework states that its safety commitments track and prepare for capabilities that create new risks of severe harm"
  • Propensity evaluations (alignment evaluations): Tests assessing what a model tends to do by default across choices. "Propensity evaluations, also known as alignment evaluations, assess what a model tends to do by default"
  • Reference Class Forecasting (RCF): Forecasting using outcomes from similar past cases to counter biases. "Reference Class Forecasting (RCF) predicts outcomes by examining what typically happened in similar past situations"
  • Rogue AI: Harmful behavior originating within AI systems themselves, such as deception or loss of control. "Finally, rogue AI risks concern harmful behaviour originating within the AI systems themselves, such as goal misalignment, deception, or loss of control as capabilities increase."
  • Scenario planning: Technique that develops multiple plausible future scenarios to test strategies and risks. "We then propose threat modelling strategies for African contexts, surveying reference class forecasting, structured expert elicitation, scenario planning, and system theoretic process analysis"
  • Situational awareness: A capability where a model understands its environment, status, and potential impacts. "Capability evaluations measure what a model is able to do and how those abilities inform its potential risk profile, testing for abilities such as deception, persuasion and manipulation, situational awareness, and political strategy."
  • Sociotechnical systems: Interlinked social and technical components in which AI operates. "their tendency to test algorithmic outputs in isolation from the sociotechnical systems in which AI operates"
  • Structured Expert Elicitation (SEE): Method to aggregate expert judgments with uncertainty quantification when data is scarce. "Structured Expert Elicitation (SEE) enables predictions in the absence of historical data by systematically gathering and synthesizing expert judgement."
  • Suddenness: The rapid onset of harm that overwhelms coping and governance capacity. "We distinguish severe risks by amplification and suddenness, where amplification requires that frontier AI be a necessary magnifier of latent danger and suddenness captures harms that materialise rapidly enough to overwhelm ordinary coping and governance capacity."
  • System theoretic process analysis (STPA): Safety analysis method examining how system interactions and controls can lead to hazards. "We then propose threat modelling strategies for African contexts, surveying reference class forecasting, structured expert elicitation, scenario planning, and system theoretic process analysis"
  • System-centric models: Threat modelling approach analyzing vulnerabilities that emerge from interactions among system components. "System-centric models examine how interactions between system components create vulnerabilities."
  • Threat-centric models: Threat modelling approach focusing on attackers’ characteristics, capabilities, and motivations. "Threat-centric models emphasize the characteristics, capabilities, and motivations of potential attackers."
  • Threat modelling: Structured practice of identifying, analyzing, and mitigating risks and attack paths. "How these models are constructed, however, depends on the threat-modelling techniques employed."
  • Unknown unknowns: Risks that are unanticipated or unobservable within current evaluation protocols. "Similarly, there is concern about 'unknown unknowns'."
  • Vulnerability: Weakness in a system that allows a hazard to cause harm. "A hazard is the source of danger; a vulnerability is the weakness within a system that allows the danger to materialise"
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