Michigan MiDAS unemployment fraud-detection algorithm failure
Michigan's automated unemployment-fraud detection system falsely accused more than 34,000 people of fraud over two years, an 85% error rate, triggering wage garnishments, bankruptcies, and lawsuits.
The damage was public. The root cause was preventable.
Michigan's automated unemployment-fraud detection system falsely accused more than 34,000 people of fraud over two years, an 85% error rate, triggering wage garnishments, bankruptcies, and lawsuits.
What the software actually got wrong
The state removed human caseworkers from the fraud-adjudication process and let the MiDAS software issue fraud determinations automatically based on incomplete or ambiguous claimant data, with no meaningful human review before harsh penalties were imposed.
The state removed human caseworkers from the fraud-adjudication process...
The state removed human caseworkers from the fraud-adjudication process and let the MiDAS software issue fraud determinations automatically based on incomplete or ambiguous claimant data, with no meaningful human review before harsh penalties were imposed.
Requs AI Edge Case flags this exact pattern at the requirements and architecture stage — before a single line of code implementing it exists — so the assumption behind it gets challenged while it is still cheap to fix.
Beyond code coverage and "shall" testing
Root causes like this one rarely show up in code coverage or requirements-compliance testing, because nobody wrote a requirement anticipating the specific edge case that broke. Requs AI Edge Case and Requs AI Software FMEA are built to surface exactly this class of overlooked failure mode — before the software is written.
Surfaces this before code exists
Identifies edge cases like this one at requirements and architecture time, using the Common Defect Enumeration to catalog failure patterns seen across hundreds of real-world software failures — including this one.
Connects the failure mode to the hazard
Traces this class of root cause directly to the system-level hazard it can produce, so a defect pattern like this one gets flagged during design review instead of after it ships.
Find the overlooked root causes before they ship.
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