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In 2018, Salta province in Argentina was associated with a public-sector platform intended to help prevent school dropout and teenage pregnancy. Governor Juan Manuel Urtubey said it could identify, five or six years ahead, a girl who was “86% predestined” to become pregnant as a teenager. That was a political claim—not a validated accuracy score. A review by the University of Buenos Aires’ Applied Artificial Intelligence Laboratory (LIAA) found serious flaws in the methodology described for the system, leaving its claimed predictive performance unestablished.
What was the Salta platform?
The system was called the Plataforma Tecnológica de Intervención Social (Technological Platform for Social Intervention). The 2022 report Not My AI describes it as a project between Salta’s Ministry of Early Childhood and Microsoft, with stated aims that included preventing school dropout and teenage pregnancy. That report says it was piloted in Salta municipality from 2015; a 2022 Futurism account of Wired’s investigation says the province approved development in 2018. The sources describe these milestones differently and do not explain the timeline in further detail.
The available reporting does not establish Microsoft’s exact technical role, or show that it controlled every decision about how the platform was used. It describes a public-sector intervention project developed through a partnership, not a consumer product.
What information did it use?
Reporting describes records focused on girls, including those aged 10–19, and lists demographic, educational, and household characteristics. Not My AI names age, neighborhood, ethnicity, country of origin, education of the household head, disability, household size, and access to hot water. Futurism’s summary of Wired’s investigation also lists age, ethnicity, disability, country of origin, and whether a home had hot water in its bathroom. These are descriptions in the reporting; the sources do not provide a complete technical specification of the data or model.
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Using sensitive characteristics to identify people for intervention raises questions about how the system distinguished need from risk, and how its predictions might affect those identified. The sources reviewed do not establish what officials did with the data after collection or what consequences followed for any individual.
What did the governor mean by “86% predestined”?
In a television appearance on April 11, 2018, Governor Juan Manuel Urtubey said the technology could show “five or six years” ahead, by name, surname, and address, which girl was “86% predestined” to have a teenage pregnancy. LIAA quoted his statement in its September 20, 2018 review.
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The wording does not establish that the system was 86% accurate. It is Urtubey’s public description, and the sources do not provide an independently validated accuracy statistic for the platform. LIAA said the methodology associated with the claim contained serious technical and conceptual errors that called the reported results into question.
Why did LIAA challenge the prediction claim?
The evaluation data may have been contaminated
According to LIAA, the described method used SMOTE—a technique that creates synthetic examples to address imbalanced datasets—before dividing the data into training and evaluation sets. That order can allow near-duplicate examples to appear in both sets. If the evaluation set contains examples too similar to training data, reported performance can look better than performance on genuinely independent cases. LIAA concluded that the procedure could not establish the system’s true accuracy.
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The outcome data could be incomplete or unevenly reported
Pregnancy is sensitive information and may be concealed or underreported. LIAA warned that differences in reporting could make the available records unrepresentative across social groups. That is a data-quality concern, not a measured estimate of how large any bias was or proof that a particular group was misclassified at a particular rate.
The survey did not establish a forecast years into the future
LIAA said the described survey asked about pregnancy that had already occurred or was ongoing when the survey was taken. Those answers do not, by themselves, show whether a person will become pregnant five or six years later. Testing that kind of forecast requires data gathered before the outcome and a method that evaluates predictions against events occurring at the stated future horizon.
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Together, these issues undermine the public performance claim; they do not amount to a formal causal study of harm caused by the platform. The Not My AI report also characterizes the reported accuracy as artificially inflated and warns that data limitations could stigmatize poor women and girls.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What is known about visits and data handling?
Futurism’s summary of Wired’s investigation reports that “territorial agents” visited people identified by the system, photographed them, and recorded GPS locations. It also reports that no formal impact assessment had been made. These are reported operational details; the sources reviewed do not establish the full deployment practice or explain how those records were stored, shared, retained, or deleted.
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The reviewed reporting also does not establish whether the platform remains active, whether it was used in later deployments, or what ultimately happened to the collected records. Those points remain unresolved in the available accounts.
How to judge a claim like this
A confident prediction is not evidence that a system works. For a claim about identifying specific people years before a sensitive outcome, the key questions are whether evaluation used genuinely independent cases, whether the outcome data were complete and representative, and whether the model was tested against events occurring over the promised forecast period. It also matters whether public authorities assessed possible impacts and clearly disclosed how personal data would be handled. In Salta’s case, LIAA’s review directly challenged the described evaluation method and the fit between the survey data and the claimed forecast horizon.
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