Closing the Gap: How Local Context Improves AI Performance in Emerging Regions

A critical challenge has emerged in the evolving world of artificial intelligence: the global disparity in AI model performance. As AI systems become increasingly integrated into our daily lives, from healthcare to finance to education, it’s crucial that these systems work effectively for all populations, not just those in developed Western nations. However, the reality is that many AI models struggle to perform adequately in emerging markets, particularly in regions like Africa, Asia, and Latin America.

This performance gap isn’t due to any inherent limitation of AI technology. Instead, it’s a direct result of the data used to train these models. The majority of AI systems are developed using datasets that predominantly represent Western contexts, leading to models that excel in these environments but falter when faced with the diverse linguistic, cultural, and socioeconomic landscapes of emerging markets.

This article explores how integrating diverse, region-specific data can dramatically improve AI applications in emerging markets, using Africa as a compelling case study. As the topic unrolls, we’ll unroll why AI models need locally relevant data, how this data can be ethically sourced and integrated, and the transformative impact it can have on AI performance.

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Before you continue…

GeoPoll is conducting a comparative study of AI-simulated surveys and traditional CATI in Kenya. The study, whose paper will be out in a couple of weeks, is investigating the effectiveness, efficiency, and data quality generated by AI models compared to traditional human-led surveys. We want to ascertain if AI-simulated surveys can provide data as reliable and nuanced as traditional respondent surveys, how AI models simulate human-like survey responses when controlled for demographics, and the differences in response rates, data consistency, and cost efficiency between AI-driven and human-led surveys. The survey itself explores various real aspects such as nutrition and food security, media consumption and internet usage, eCommerce, AI usage and opinions, and attitudes towards humanitarian aid in the country. 

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The Global AI Performance Gap

The disparity in AI performance between developed and emerging markets is a concern in the tech industry. This gap manifests in various ways:

1. Language Processing: Many AI models struggle with languages and dialects prevalent in emerging markets. For instance, a model trained primarily in English may falter when processing Swahili or colloquial Arabic. Even the English accents vary from country to country – Nigerians speak English in a different way from South Africans, who speak differently from Americans.
2. Cultural Context: AI systems often misinterpret cultural nuances, idioms, and social norms unique to emerging markets, which leads to inappropriate or ineffective responses.
3. Economic Disparities: Models trained on data from high-income countries may make incorrect assumptions about spending patterns, access to resources, or financial behaviors in emerging economies.
4. Technological Infrastructure: AI applications designed for high-speed internet and advanced devices may underperform in regions with limited connectivity or older technology.
5. Diverse Data Representation: The lack of diverse training data leads to biased outcomes, potentially reinforcing stereotypes or excluding minority groups within emerging markets.

This performance gap has real-world consequences. In healthcare, it could mean misdiagnoses or ineffective treatment recommendations. In finance, it might result in unfair loan rejections or inaccurate credit scoring. In education, it could lead to curriculum recommendations that don’t align with local educational standards or cultural values. In marketing, you might have seen distorted AI-generated images of people from some regions of the world.

The root cause of this disparity lies in the data used to train these AI models. Datasets predominantly sourced from Western countries fail to capture the complexity and diversity of emerging markets. This data bias creates a self-perpetuating cycle: AI systems perform poorly in these markets, leading to less adoption and fewer opportunities to gather relevant data, further widening the performance gap.

Addressing this issue is not just a matter of fairness; it’s a business imperative. As emerging markets continue to grow and play increasingly significant roles in the global economy, the need for AI systems that can effectively operate in these diverse contexts becomes crucial for companies looking to expand their reach and impact.

The Importance of Local Context in AI

To truly understand why local context is crucial for AI performance, we need to delve into the nature of AI systems and how they learn:

1. Data-Driven Learning: AI models, particularly machine learning and deep learning systems, learn from the data they’re trained on. They identify patterns, correlations, and rules based on this data. If the training data lacks diversity or local context, the resulting model will have blind spots and biases.
2. Contextual Understanding: Language, behavior, and decision-making are deeply rooted in cultural and socioeconomic contexts. An AI model needs exposure to these contexts to accurately interpret and respond to inputs from diverse user bases.
3. Avoiding Misinterpretation: Without local context, AI systems may misinterpret user inputs or produce inappropriate outputs. For example, a chatbot trained on Western data might not understand the nuances of politeness in Asian cultures, leading to perceived rudeness or miscommunication.
4. Relevance of Recommendation: In applications like e-commerce or content recommendation, understanding local preferences, trends, and availability is crucial for providing relevant suggestions to users.
5. Ethical Considerations: AI systems that lack local context may inadvertently perpetuate biases or make decisions that are unethical or unfair when applied to different cultural settings.
6. Regulatory Compliance: Different regions have varying regulations around data privacy, financial practices, and other areas where AI is applied. Models need to be trained on locally relevant data to ensure compliance with these regulations.

Incorporating local context into AI models isn’t just about improving performance metrics; it’s about creating systems that are truly useful and trustworthy for users in emerging markets. This approach leads to:

• Improved User Experience: AI applications that understand local context provide more accurate, relevant, and culturally appropriate responses, enhancing user satisfaction and adoption.
• Increased Efficiency: Locally-aware AI systems can streamline processes and decision-making in ways that are optimized for the specific market, leading to greater efficiency and cost-effectiveness.
• Innovation Opportunities: Understanding local contexts can show unique use cases and innovative applications of AI that may not be apparent when viewing the market through a Western-centric lens.
• Social Impact: Accurately serving the needs of emerging markets makes AI a powerful tool for addressing local challenges in areas like healthcare, education, and financial inclusion.

The key to achieving these benefits lies in sourcing high-quality, diverse data that accurately represents the target markets. This is where companies like GeoPoll play a crucial role, providing the essential local context that can transform AI performance in emerging markets.

AI in Africa

Africa serves as a compelling example of both the challenges and opportunities in adapting AI for emerging markets. With its diverse languages, cultures, and economic conditions, the continent presents a unique landscape for AI development and deployment.

Challenges:

1. Linguistic Diversity: Africa is home to over 3,000 languages. Many AI models struggle with this linguistic complexity, especially with languages with limited digital presence. The accents are diverse even in global languages such as English, French, and Arabic, which are widely spoken in Africa.
2. Infrastructure Limitations: Varying levels of internet connectivity and device access across the continent pose challenges for AI applications designed for high-bandwidth environments.
3. Economic Disparities: The wide range of economic conditions across and within African countries requires AI models to be adaptable to different socioeconomic contexts.
4. Data Scarcity: There’s a general lack of large-scale, quality datasets representing African users, which has historically limited the development of locally relevant AI models.

Opportunities and Success Stories:

Despite these challenges, there are promising developments in AI across Africa:

1. Natural Language Processing (NLP): Projects like Lelapa and Masakhane are working on developing NLP models for African languages, improving machine translation and text analysis capabilities.
2. Healthcare: AI is being used to enhance diagnostic capabilities in resource-limited settings. For example, a model trained on local data has shown promise in diagnosing malaria from smartphone images of blood samples.
3. Agriculture: AI-powered apps are helping farmers predict weather patterns, detect crop diseases, and optimize resource use, contributing to food security efforts.
4. Financial Inclusion: AI models adapted to local economic behaviors are improving credit scoring systems, enabling more accurate risk assessment for individuals without traditional credit histories.
5. Education: Adaptive learning platforms using AI are being developed to cater to diverse educational needs across the continent, considering local curricula and learning styles.

There exists a huge transformative potential of AI when powered by contextually rich, local data. They also highlight the immense value that companies like GeoPoll can provide by offering access to diverse, high-quality datasets from across the African continent.

As AI continues to evolve and expand in Africa, the integration of local context through relevant data will be crucial in creating systems that truly serve and empower African users, bridging the global AI performance gap.

GeoPoll’s Role in Bridging the Gap

GeoPoll stands at the forefront of addressing the AI performance gap in emerging markets, particularly in Africa. With its extensive experience in conducting surveys and collecting data across diverse populations, GeoPoll is uniquely positioned to provide the critical ingredient for improving AI performance: high-quality, locally relevant data.

Key Contributions:

1. Diverse Data Collection: GeoPoll’s methodologies allow for the collection of data from a wide range of demographics, including hard-to-reach populations. This ensures that AI models trained on this data are truly representative of the target markets.
2. 1 million hours of African voice recordings – GeoPoll holds an unmatched database of authentic African voice recordings from our surveys. We have over a million hours of voice recordings, in over 40 languages from all African countries. Combined with transcripts and possible translations, this is an invaluable asset from anyone looking to train LLMs based on African languages.
3. Multi-Modal Data: GeoPoll collects data through various channels, including voice, SMS, and online surveys. This multi-modal approach captures a more comprehensive picture of user behaviors and preferences.
4. Real-Time Insights: The company’s ability to gather real-time data allows for the creation of AI models that can adapt to rapidly changing market conditions and consumer behaviors.
5. Ethical Data Practices: GeoPoll adheres to strict ethical standards in data collection, ensuring that the data used for AI training respects privacy and consent, crucial for building trust in AI systems.
6. Local Expertise: With teams on the ground in many African countries, GeoPoll brings invaluable local knowledge to the data collection process, ensuring cultural nuances are properly captured.

Impact on AI Development:

By leveraging GeoPoll’s data, AI developers can:

1. Improve Language Models: Train NLP models on real-world usage of local languages and dialects, improving translation, sentiment analysis, and chatbot performance.
2. Enhance Predictive Analytics: Develop more accurate predictive models for consumer behavior, market trends, and economic indicators in emerging markets.
3. Refine Recommendation Systems: Create more relevant and culturally appropriate recommendation algorithms for e-commerce, content delivery, and personalized services.
4. Optimize Decision-Making AI: Improve the accuracy of AI-driven decision-making tools in areas that define the day-to-day activities of Africans, as well as business decisions.

The Bottomline

The global AI landscape is at a pivotal juncture. As we’ve explored throughout this article, the performance gap between AI systems in developed markets and emerging economies is not just a technological challenge – it’s an opportunity for innovation, inclusion, and impactful change.

The key to bridging this gap lies in recognizing the paramount importance of local context. AI systems, no matter how advanced, can only be as good as the data they’re trained on. In the diverse, complex environments of emerging markets like Africa, this means going beyond surface-level data collection to truly understand the nuances of language, culture, economic conditions, and social dynamics.

GeoPoll, with our extensive experience and innovative methodologies in data collection across emerging markets, is a crucial partner in this endeavor. We can provide rich, locally relevant datasets to enable the development of AI systems that don’t just work in these markets – they thrive, offering solutions tailored to local needs and challenges.

Learn more about GeoPoll AI Data Streams and voice recordings. Contact us to discuss how our data can slot into your AI project.