COMPAS criminal-recidivism risk-assessment algorithm bias
An investigation found that a widely used US criminal-justice risk-assessment algorithm was significantly more likely to incorrectly flag Black defendants as high-risk for future crime than white defendants, despite similar actual reoffense rates.
The damage was public. The root cause was preventable.
An investigation found that a widely used US criminal-justice risk-assessment algorithm was significantly more likely to incorrectly flag Black defendants as high-risk for future crime than white defendants, despite similar actual reoffense rates.
What the software actually got wrong
The algorithm's underlying statistical model, trained on historical data reflecting existing disparities in the justice system, produced systematically unequal error rates across racial groups, and the tool was deployed in real sentencing and parole decisions without adequate scrutiny of this bias.
The algorithm's underlying statistical model, trained on historical data...
The algorithm's underlying statistical model, trained on historical data reflecting existing disparities in the justice system, produced systematically unequal error rates across racial groups
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.
The tool was deployed in real sentencing and parole...
the tool was deployed in real sentencing and parole decisions without adequate scrutiny of this bias
Requs AI Software FMEA traces this failure mode back to the system-level hazard it feeds, tagging it against the Common Defect Enumeration so it surfaces in review instead of in the field.
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.
Schedule a demonstration, or explore how Requs AI Edge Case and Software FMEA use the Common Defect Enumeration.