policy
The Requirements Appeared After the Model Did
A 15-month public-sector case study finds that only 40 percent of reconstructed ML requirements were ever formalized.
Summary
A 15-month public-sector case study finds that only 40 percent of reconstructed ML requirements were ever formalized.
Researchers traced 95 requirements and acceptance criteria across 43 artifacts and six interviews around Brazil's national digital-participation platform. Most key requirements emerged from experiments, outputs and integration work, while the classification taxonomy was chosen partly for institutional legitimacy. Technical metrics, domain judgment and political assessment never converged on shared thresholds or proven practical usefulness. The case argues that experiments and institutional artifacts are part of requirements engineering, not steps after it.
Why it matters
A 15-month public-sector case study finds that only 40 percent of reconstructed ML requirements were ever formalized.
Limits and context
- The case argues that experiments and institutional artifacts are part of requirements engineering, not steps after it.
Key claims
A 15-month public-sector case study finds that only 40 percent of reconstructed ML requirements were ever formalized.
Qualification: The case argues that experiments and institutional artifacts are part of requirements engineering, not steps after it.
Evidence: source-2026-11-07-009
Sources
- arXiv preprint 2611.07951, original v1arXiv · primary research
Corrections
: Publication note: This November 7 edition was assembled before its scheduled 9:00 AM America/Detroit reveal but is being published late after recovery from a local build interruption. Its original reporting, image-generation times and scheduled publishAt are preserved.