Kenya was promising wider access to more affordable healthcare. But a new investigation has revealed that its algorithm-driven system is making life harder for the people it was supposed to help most. According to reporting by The Guardian, Africa Uncensored, and Lighthouse Reports, Kenya’s new Social Health Authority system is using a predictive machine-learning algorithm to estimate how much people should pay for public health insurance.
The system was first launched back in October 2024 as part of President William Ruto’s promise to expand healthcare access to Kenya’s large informal workforce.
How the algorithm is hurting Kenyans
The problem is how the system calculates what people can afford. Kenya’s SHA system uses proxy means testing, which is a method that estimates income based on household details such as roofing materials, toilets, livestock, family size, and other living conditions. This investigation uncovered that the system has been overestimating the incomes of poorer households, while underestimating them for wealthy citizens.
One SHA volunteer described visiting households in Nairobi and watching people who were already struggling to afford food receive premiums far beyond their means. Some faced charges equal to 10% to 20% of their small incomes, according to the report.
When a bill blocks treatment
The consequences are real, and the situation seems dire. Kenyans without private insurance who cannot pay their SHA premiums risk being turned away from health facilities or receiving steep hospital bills. The report cites accounts of critically ill people missing treatment because the system said they owed more than they could pay. One single mother said her monthly contribution was set at 3,500 Kenyan shillings, while others reported large jumps from what they previously paid under the old system. So the new policy is costing lives.
Although Ruto has described the system as AI-powered, the report notes that it is not using ChatGPT-style generative AI. It uses predictive machine learning, built around a decades-old policy tool that has long been criticized for misidentifying who qualifies for help. Many have called this system flawed and inequitable even before it was deployed.
More than 20 million people are registered for SHA, but only about 5 million regularly pay their premiums. Hospitals are also reporting large deficits as reimbursements remain unpaid. This is the danger of algorithmic welfare systems.
