1. Australia Just Paid A$548 Million for Robodebt. It Wasn’t AI. The Lesson…
Source: Matt Sheehan, SpatialNext (LinkedIn), October 2026
Summary: Robodebt happened in three phases: people checking and overwhelmed, people taken out, people put back. None of the three ever decided which decisions the system could make on its own. The piece walks through how a student with a summer job would have been treated if that line had been drawn first.
Link: https://www.linkedin.com/pulse/australia-just-paid-a548-million-robodebt-wasnt-ai-lesson-sheehan-qgtzc/
2. Using new imaging technologies to monitor the Common Agricultural Policy (Special Report 04/2020)
Source: European Court of Auditors, 2020
Summary: This is the report that set the scene for the system in this week’s newsletter. The auditors found that satellite “checks by monitoring” could cover every farmer instead of a small sample, and let farmers correct claims before they were finalised. They also flagged the weak spot: many parcels, especially small plots and grazing land, can’t be settled by imagery alone, so someone has to decide what happens next.
Link: https://op.europa.eu/webpub/eca/special-reports/new-tech-in-agri-monitoring-4-2020/en/index.html
3. Xenophobic Machines: Discrimination through unregulated use of algorithms in the Dutch childcare benefits scandal
Source: Amnesty International, October 2021
Summary: The Dutch tax authority’s risk algorithm flagged families for possible fraud, and flagged families had their benefits suspended. The civil servants reviewing the flags were given no explanation for the scores. Tens of thousands of parents were wrongly accused, and the government resigned. It’s the clearest case of a flag carrying a financial consequence before anyone had properly looked.
Link: https://www.amnesty.org/en/latest/news/2021/10/xenophobic-machines-dutch-child-benefit-scandal/
4. The flaws of policies requiring human oversight of government algorithms
Source: Ben Green, Computer Law & Security Review, 2022
Summary: Green reviewed 41 policies that require a human to oversee government algorithms. He found that people often can’t provide the oversight these policies assume, and that the requirement can make flawed systems look legitimate. His answer is to make agencies justify, with evidence and before adoption, both the algorithm and the human role. That’s close to your argument for drawing the line before launch.
Link: https://arxiv.org/abs/2109.05067
If your organisation runs a system that flags, scores or approves, and you’re not sure where its line sits, that’s the work I do. I help teams decide, before launch, which decisions the system can make alone and which need a person. Get in touch at mattsheehan@spatialnext.io.