
Ghana made history when it led the call at the United Nations for reparations. Ghana has become Africa's leading voice on the issue, tabling a UN resolution and hosting a summit in Accra with delegates from around 80 countries. I believe the next chapter of that conversation is not about the past alone. It is about the algorithms that now shape who gets heard, hired, lent money, and believed. I call it algorithmic reparations.
The idea is not mine alone
Let me be clear about where this conversation began. The term was developed by Jenny L. Davis, Apryl Williams, and Michael W. Yang in a 2021 paper. They argue that reparative algorithms should name, unmask, and undo the harms that machine learning systems cause, and that "fairness" fixes fall short. Davis and Williams have since expanded the argument into a book, The Injustice of Fairness. Their work centres on structural inequality inside the systems. I want to add an African angle to it.
Where the Global South enters the story
AI is often described as pure machine genius. The human supply chain behind it tells a different story. In 2023, a TIME investigation reported that OpenAI used outsourced Kenyan workers earning less than $2 an hour to make ChatGPT less toxic. The workers, hired through the firm Sama, reportedly took home between about $1.32 and $2 an hour depending on seniority and performance. Four workers told TIME they were mentally scarred by the content they had to review.
The story has two sides. A Sama spokesperson disputed the workload figures and said workers could earn between $1.46 and $3.74 an hour after taxes. OpenAI said some workers earned more than $2 an hour, and that filtering harmful material is a necessary step in making AI safer. Even so, the pattern is hard to ignore. Wealthy firms build products worth billions, and much of the hidden labour comes from poorer countries.
The data question
There is a second issue that African scholars keep raising: whose knowledge goes into these systems, and whose is missing. A 2026 review of African scholarship on responsible AI points to the marginalisation of African epistemologies, the absence of representative African datasets, and weak locally grounded regulation. One Just AI fellow put it plainly: African languages are not merely datasets, but expressions of identity, culture, history, and sovereignty.
I argue that when global AI products benefit from the labour and cultural output of the Global South, whether paid, underpaid, or scraped, fairness in the code is not enough. Redress is also owed. I will be honest that the full evidence on how much African creative work sits inside training data is still being gathered. That is exactly why we need transparency laws, so researchers can check.
What reparations would look like
I am not talking about a one-time cheque. I mean practical steps:
- Fair pay and mental-health protection for African data workers.
- Transparency about what data trains major models and where the labour comes from.
- Investment in African languages and datasets, owned and governed by Africans.
- A seat at the table for African researchers and regulators in global AI governance.
The honest debate
Not everyone agrees. A 2024 paper in Big Data & Society argues that algorithmic reparation can be too narrow, and may fall short when an algorithm is not the only cause of a harm. That is a fair challenge, and it is why I want this movement built on evidence, not slogans.
Ghana already knows how to ask the world a hard question about debts and dignity. Let us ask the next one: when the machines learn from us, who benefits, and who is repaid?



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