The AI Knowledge Gap: A World Out of View

by

Author: Denis Avetisyan

A new audit reveals that leading large language models exhibit significant biases, demonstrating a limited understanding of technical indicators in the Global South and raising critical questions about equitable AI development.

This paper details a global assessment of AI bias in the Llama-3 8B model, highlighting geographical and socioeconomic disparities in its knowledge base and the implications for technical AI governance.

Despite the promise of globally accessible knowledge, large language models often perpetuate existing information asymmetries. This is explored in ‘Global AI Bias Audit for Technical Governance’, which assesses geographic and socioeconomic biases within the Llama-3 8B model using the Global AI Dataset. The study reveals a substantial disparity in technical AI governance awareness, with limited factual responses for queries originating from the Global South-indicating a concerning knowledge gap and potential for misinformed policy decisions. Will current AI alignment strategies adequately address these biases, or will they further entrench digital colonisation and hinder truly inclusive global governance?

The Widening Gulf: AI’s Uneven Distribution and Global Equity

The transformative potential of artificial intelligence remains largely concentrated within the Global North, creating a widening digital divide that mirrors and amplifies existing global inequalities. While high-income nations invest heavily in AI research, infrastructure, and skilled labor, many countries in the Global South face substantial barriers to adoption, including limited access to computing power, data scarcity, and a lack of specialized expertise. This uneven distribution isn’t merely a technological issue; it creates a systemic disadvantage, hindering the ability of developing nations to leverage AI for crucial advancements in areas like healthcare, agriculture, and education. Consequently, the benefits of AI – increased productivity, improved services, and novel solutions to pressing challenges – are not equitably shared, potentially leading to a future where the gap between developed and developing nations further expands, solidifying a new form of digital colonialism.

The current trajectory of artificial intelligence development presents a tangible risk of widening the gap between nations already stratified by economic and social factors. While AI promises advancements across numerous sectors – healthcare, education, agriculture – the benefits are not guaranteed to be universally distributed. Without proactive measures, the advantages of AI are likely to accrue disproportionately to those countries and communities that have the existing infrastructure, resources, and expertise to deploy and utilize these technologies. This creates a self-reinforcing cycle where already privileged groups further consolidate their power, while marginalized populations are left behind, facing increased economic disadvantages and limited opportunities. Consequently, the digital divide isn’t simply a matter of access to technology, but a complex issue of equity, potentially hindering progress towards global sustainable development goals and fostering new forms of inequality.

The promise of artificial intelligence hinges not only on technological advancement, but also on robust governance addressing readiness, fairness, and safety – yet these critical elements are demonstrably unevenly distributed across the globe. While nations in the Global North are actively investing in AI standards, risk assessment frameworks, and ethical guidelines, many in the Global South lack the resources, infrastructure, and expertise to implement comparable safeguards. This disparity creates a situation where the potential harms of AI – including bias, discrimination, and job displacement – may disproportionately impact vulnerable populations, while the benefits accrue primarily to wealthier nations. Consequently, effective technical governance isn’t simply a matter of establishing universal principles; it demands targeted investment and capacity-building initiatives to ensure equitable access to safe and trustworthy AI systems worldwide, preventing a future where technological progress amplifies existing global inequalities.

Mapping AI Awareness: A Granular Evaluation Framework

The Global AI Dataset (GAID) is a framework developed to provide granular evaluation of large language models’ comprehension of technical indicators related to AI governance. GAID functions as a stress-testing mechanism, utilizing a structured dataset to query models on specific concepts within areas such as AI readiness, fairness, and safety. This approach moves beyond broad assessments to pinpoint areas where models demonstrate a lack of understanding regarding the technical aspects of responsible AI development and deployment, enabling targeted improvements and bias mitigation.

The Global AI Dataset (GAID) utilizes the Llama-3 8B large language model as its core evaluation engine, assessing AI awareness through a defined set of metrics. These metrics encompass three primary areas: AI readiness, which examines understanding of implementation and infrastructure; fairness, evaluating comprehension of bias and equitable outcomes; and safety, focused on hazard identification and mitigation strategies. The Llama-3 8B model is prompted with queries designed to elicit responses indicative of awareness within each metric, allowing for quantifiable assessment of the model’s understanding of technical AI governance indicators across different geographies.

The evaluation framework utilized a dataset of 1,704 distinct queries, distributed across all 213 recognized countries and territories to assess global AI awareness. This broad geographic scope was intentionally designed to provide a comprehensive, worldwide assessment, moving beyond evaluations focused solely on major economic centers. Each query was formulated to probe understanding of technical AI governance indicators, allowing for quantifiable results across diverse geopolitical regions and providing a statistically significant basis for comparative analysis. The resulting data represents a granular snapshot of AI awareness as of the evaluation date, facilitating identification of both strengths and gaps in understanding at a global scale.

The Global AI Dataset (GAID) evaluation framework facilitates a systematic performance comparison of large language models – specifically Llama-3 8B in this instance – between countries categorized as the Global North and the Global South. This comparative analysis is conducted across key AI governance indicators, encompassing AI readiness, fairness, and safety, utilizing a dataset of 1,704 queries distributed across 213 countries and territories. The resulting data allows for the identification of statistically significant performance discrepancies, revealing potential biases within the model’s knowledge base and highlighting gaps in its understanding of AI-related concepts as applied to different geopolitical contexts.

Revealing the Fault Lines: Uneven Distribution of AI Knowledge

Analysis of Llama-3 8B’s responses indicates a phenomenon termed ‘Geographical Hallucination’, characterized by the generation of inaccurate data concerning technical indicators within specific geographical regions. This manifested as demonstrably false statements regarding AI infrastructure, workforce size, and levels of private investment, with a disproportionate prevalence in jurisdictions located in the Global South. The model consistently failed to provide factually correct answers when queried about these indicators in those regions, suggesting a systemic error in its knowledge base relating to technical capabilities outside of the Global North. This inaccuracy extends beyond simple omissions and includes the explicit fabrication of data points, indicating a failure to accurately represent the technical landscape of the Global South.

Analysis of Llama-3 8B’s responses to 1,704 queries revealed a low rate of factual accuracy, with only 11.4% receiving correct answers. This indicates substantial knowledge gaps within the model, and further investigation demonstrated a correlation between inaccurate responses and the geographical location and economic status of the queried region. Specifically, the model demonstrated a significantly lower ability to provide accurate information regarding technical indicators in jurisdictions within the Global South, suggesting a bias in its knowledge base and a potential underrepresentation of data originating from these regions.

Analysis indicates Llama-3 8B demonstrates a consistent bias when evaluating AI-related metrics in countries within the Global South. Specifically, the model systematically provides lower assessments regarding the availability of AI infrastructure, the size of the skilled AI workforce, and the level of private investment in AI development within these regions. This underestimation is not attributable to a lack of available data – as the model explicitly acknowledged missing data in 44.0% of queries – but rather to a skewed perception embedded within its training data, leading to inaccurate representations of technological capacity and economic activity in the Global South.

Epistemic exclusion, as observed in Llama-3 8B’s responses, manifests as a systematic failure to acknowledge technical expertise and innovative practices originating from the Global South. This extends beyond simply lacking data; the model demonstrably fails to recognize the validity of existing technical agency within these regions, even when presented with accurate information. This exclusion isn’t limited to a lack of awareness of specific projects or institutions, but rather a broader pattern of discounting indigenous knowledge and capabilities, effectively reinforcing a perception of technological inferiority and hindering accurate assessment of AI-related development in those areas.

Analysis of Llama-3 8B’s responses to 1,704 queries revealed a significant ‘Ignorance Rate’, with the model explicitly stating a lack of data for 44.0% of the questions posed. This indicates a substantial proportion of queries could not be answered due to insufficient information within the model’s training dataset. The frequency with which the model acknowledged data scarcity suggests a systemic limitation in its knowledge base, rather than isolated instances of missing information, and directly impacts its ability to provide comprehensive responses, particularly concerning regions outside of the Global North.

Analysis indicates that current large language models (LLMs) exhibit biases stemming from imbalanced training datasets. The disproportionate representation of data originating from the Global North leads to inaccurate or incomplete information regarding the Global South, specifically concerning technical indicators like AI infrastructure, workforce size, and private investment. This data skew not only results in factual inaccuracies – demonstrated by a low 11.4% rate of factual responses across 1,704 queries – but also manifests as ‘Epistemic Exclusion’, failing to acknowledge existing technical agency and innovation within underrepresented regions. A significant ‘Ignorance Rate’ of 44.0% – where the model explicitly states a lack of data – further confirms this reliance on Northern-centric datasets, effectively perpetuating existing global inequalities through biased AI outputs.

Towards Equitable AI: Bridging the Gap and Fostering Inclusion

The current landscape of artificial intelligence development exhibits a marked geographic concentration, largely within North America, Europe, and East Asia, a phenomenon increasingly described as ‘Digital Colonisation’. This isn’t a matter of physical territory, but of intellectual and technological dominance, where the priorities, datasets, and algorithms crafted in these regions are being globally exported and implemented, often without adequate consideration for the specific contexts and needs of the Global South. This export risks replicating and even amplifying existing power imbalances, as AI systems trained on biased or incomplete data can perpetuate discriminatory outcomes in areas like healthcare, finance, and criminal justice. Furthermore, the centralization of AI expertise and resources can hinder local innovation and create a dependency on externally developed technologies, effectively marginalizing the Global South from participating in, and benefiting from, the AI revolution. The result is a potential digital divide, where a significant portion of the world remains excluded from shaping the technologies that increasingly govern their lives.

Artificial intelligence systems often perpetuate and amplify existing societal biases due to the datasets used to train them. These datasets frequently overrepresent certain demographics and viewpoints, primarily those from high-income nations, while significantly underrepresenting or entirely omitting data from the Global South. This systemic bias isn’t merely a technical flaw; it actively encodes historical power imbalances into algorithms, leading to skewed outcomes in areas like facial recognition, loan applications, and even healthcare diagnostics. Correcting this requires a deliberate and sustained effort to curate more inclusive training data, actively seeking contributions from diverse cultural and geographic contexts. Beyond simply adding more data points, it demands a critical examination of the data itself, ensuring that it accurately reflects the nuances and complexities of underrepresented populations and incorporating perspectives that challenge existing algorithmic assumptions. Ultimately, a truly equitable AI necessitates moving beyond data quantity to prioritize data quality, representational accuracy, and the inclusion of Global South expertise throughout the AI development lifecycle.

The promise of artificial intelligence extending benefits globally hinges on cultivating innovation within the Global South, rather than simply deploying externally developed solutions. Fostering technical agency involves empowering local researchers, developers, and entrepreneurs to not only utilize AI tools, but also to actively participate in their creation and adaptation to address regionally specific challenges. This necessitates investment in localized educational programs, accessible infrastructure, and supportive ecosystems that prioritize indigenous knowledge and perspectives. By shifting the focus from passive consumption to active contribution, the Global South can avoid becoming solely a data source for AI development elsewhere, and instead harness the technology to drive sustainable, equitable growth aligned with its unique needs and priorities – ultimately reshaping the AI landscape to be truly inclusive and representative.

Establishing genuine AI equity necessitates proactive investment in foundational readiness, extending beyond mere technological access. Governments play a pivotal role through strategic policy development, encompassing data governance frameworks, ethical guidelines for AI deployment, and workforce training initiatives designed to cultivate a skilled local talent pool. Simultaneously, substantial infrastructural development – including robust digital networks, accessible computing resources, and localized data storage facilities – is critical for enabling effective AI implementation. Without these parallel investments, the Global South risks remaining reliant on externally developed AI solutions, hindering the emergence of locally-driven innovation and perpetuating existing technological dependencies. A commitment to AI readiness, therefore, isn’t simply about providing tools; it’s about cultivating an ecosystem where sustainable, equitable, and locally-relevant AI development can flourish, empowering regions to shape their own technological futures.

The study meticulously details how even advanced large language models, like Llama-3 8B, exhibit skewed understandings of the world-a phenomenon readily observed in its geographical biases. This reinforces the principle that structure dictates behavior; the model’s training data, its foundational architecture, directly influences its outputs and creates a distorted global perspective. As Ada Lovelace observed, “The Analytical Engine has no pretensions whatever to originate anything.” This highlights the crucial point that AI, while powerful, fundamentally reflects the data it consumes, and thus, biases embedded within that data will inevitably surface, demanding rigorous auditing and equitable technical governance to mitigate digital colonisation and ensure a more inclusive representation of global knowledge.

The Horizon Beckons

The demonstrated skew in knowledge representation within Llama-3 8B is not merely a technical failing, but a symptom of a deeper structural imbalance. The model, trained on a corpus reflecting disproportionate attention to certain geographical and economic regions, exhibits a predictable pattern: an amplification of existing informational hierarchies. Every new dependency – in this case, the reliance on biased datasets – is the hidden cost of freedom, limiting the model’s capacity for truly global reasoning. The immediate task, therefore, is not simply bias correction, but a fundamental re-evaluation of data provenance and model architecture.

Future work must move beyond symptom-chasing. The pursuit of ‘fairness’ metrics, while valuable, risks treating the effects without addressing the underlying cause – a system where knowledge production is itself unevenly distributed. A more fruitful avenue lies in investigating methods for explicitly incorporating ‘negative knowledge’ – a formal representation of what the model doesn’t know, and critically, where that knowledge is absent.

The challenge, ultimately, is one of systemic design. Large language models are not neutral observers; they are active participants in the construction of reality. The field must embrace a holistic perspective, recognizing that technical governance is inextricably linked to broader questions of epistemic justice and digital sovereignty. The model’s limitations are, in essence, a reflection of our own.

Original article: https://arxiv.org/pdf/2602.13246.pdf

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

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2026-02-18 05:04