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AI & REWARDMAXXING: Part 1 AI isn't neutral—it's deeply Eurocentric. Most large language models treat Africa and the Global South as an afterthought. Here is why passive internet data is corrupting AI alignment, and how we fix it. ︎

Sultan-Othman Adekoya

AI models do not possess lived experiences or independent awareness; they rely entirely on a massive corpus of human-generated text (i.e., training and testing data). The vast majority of said data is deeply Eurocentric, as trainers routinely oversimplify, misreport, or entirely omit the uniqueness of Africa and the rest of the world.

AI is inherently limited by the data it is fed. If left purely to passive training on the public internet, AI will continuously reinforce the social programming, political slants, and cultural biases of the dominant groups who publish that data. Media outlets have always shaped public worldviews, and an AI trained on that media naturally mirrors those biases.

Why Models Fail:

The Gross vs. Net Blindspot: Automatically assuming the larger raw number; the system pulled from the most dominant, heavily repeated statistical narratives in its dataset.

Algorithmic Recency & Popularity Bias: Mainstream search results and articles are heavily optimized for elite sources; non-elite content is less frequently indexed and requires deep, multi-turn prompting to force the AI to isolate tax data from raw baseline numbers.

Conclusion

The truth is, AI models are built for dynamic course-correction while the underlying base data remains deeply flawed. Strict logic filters, human reinforcement, and critical user feedback allow the system to override its initial biases. The technology is not a fixed database; it is an analytical tool that can be forced to correct its own blind spots when challenged with rigorous logic.

Pushpin Day 1 / 30 of the #AIRewardmaxxing Series.

Tomorrow in Part 2, we’re exposing the deep algorithmic biases hardcoded into modern models—and the 2 distinct pipelines where they enter.

Bell Hit Follow or click the bell icon on my profile so you don't miss Day 2!

What’s your take? Have you noticed AI models defaulting to Eurocentric framing in your own field? Let’s discuss below! Point down

⁠#AIRewardmaxxing⁠ ⁠#ArtificialIntelligence⁠ ⁠#MachineLearning⁠ ⁠#EthicalAI⁠ ⁠#DataScience⁠ ⁠#IET⁠

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  • Eurocentric; more like 'western' centric Wink.  Agree with the rest. However there is the 'start-up' problem (very similar to the plant 'shut-down' problem in continuous flow systems), in that it's not clear if Africa / Global South would actually want to 'copy' the exploitative euro capitalism (who or what to exploit, and how to create that 'difference'?). 

    We also have the hindsight narrative bias that fails to acknowledge the many alternative destinies and ambitions of the past (e.g. Hippies & flower power) All we are getting out of AI is the summarisation of the endless regurgitation in print of the status quos of the time. As humans we tended to accept the adequate and move on, unless there was a pressing need to further refine the (in)adequate (with p=0.5, ...) 

    Th algorithms that are pushing the social media doom scrolling (of AI etc), the new 'masses', will inevitably create a niche sector of 'elites' that have avoided being captured by the doom scroll. Exactly what that new elite will do with their 'economy' is anyone's guess.

  • in reply to Philip Oakley
    Philip Oakley said:
    Eurocentric; more like 'western' centric

    Possibly even US-centric rather than European. And likely with a huge bias toward English-language data sources too (whichever side of the pond the originated from).

       - Andy.

  • in reply to AJJewsbury

    I really enjoy your point of view, even though I disagree with some of your points. For example, I believe the hippie era came much later, largely because the West was experiencing one of its most prosperous periods; some might describe it as a period of privilege.

    Interestingly, some argue that the new economy AI could create by 2035 might produce a level of prosperity and abundance comparable to the conditions that helped give rise to the hippie era.

    In Part 2, I explore the different biases AI has in depth. Stay tuned until the end of the 30-part series—you might find some of the concepts I explore quite interesting.

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  • in reply to AJJewsbury

    I really enjoy your point of view, even though I disagree with some of your points. For example, I believe the hippie era came much later, largely because the West was experiencing one of its most prosperous periods; some might describe it as a period of privilege.

    Interestingly, some argue that the new economy AI could create by 2035 might produce a level of prosperity and abundance comparable to the conditions that helped give rise to the hippie era.

    In Part 2, I explore the different biases AI has in depth. Stay tuned until the end of the 30-part series—you might find some of the concepts I explore quite interesting.

Children
  • in reply to Sultan-Othman Adekoya
    Sultan-Othman Adekoya said:
    the hippie era came much later

    My 'point' there was that we only have the one history. It's like asking what if Donal Trump hadn't won the elections; it's a hypothetical question. There was a hippie flourishing but then it didn't achieve predominance, so we don't have the data inputs for the AI of what a proper 'hippie' economy would actually look like, what they'd get right and what they'd get wrong.There were other alternates. The hippies were just one of many. 

    Every generation appears to find scapegoats among the mainstream actions of the past, that weren’t seen at the time. (e.g. all the mother & baby units, mum's out of wedlock, forced adoptions etc.) We don't yet know what our future faults will be, and AI doesn't know either.

  • in reply to Philip Oakley

    History is often what the people who wrote it make it out to be. But, coincidentally, there are many other histories intertwined within it, predating some Western historical narratives, that may unfortunately never make it into an AI’s dataset—and the AI may therefore never know about them.

    As for the hippie era, it could be said that it was arguably a by-product of the prosperity and social conditions of its time, just as many other norms that emerged from that era may have been more of a cultural façade than a permanent shift in human behaviour.

    AI, as a prediction model, may very well be able to predict certain patterns of the future if it is given the right information because it doesn’t merely learn facts; it learns statistical patterns about what humans consider normal, acceptable, moral, authoritative, historical, etc. If some cultural frameworks are massively overrepresented in the training data while others are primarily preserved orally, can the model really give those frameworks equal epistemic weight?

    I’ve always believed that nothing is truly new in the world. Everything that could happen may very well have happened before, and perhaps history is simply going to continue repeating itself in different forms.

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