Framework Lab — Level 100 worksheet
AIGovOps Beacon · 30-minute auditor flow
Name / date: ________________________________
For each case below, fill in: who was harmed, what failed, which framework
applies, which Beacon artifact would have mattered most, and which YES-act
(Ship / Steady / Recover) it falls under. Bring your completed worksheet
to the review session.
Case 1 — Boeing 737 MAX MCAS software fatal crashes (2018-2019)
Boeing concealed a safety-critical MCAS design change from regulators and pilots; the certification process failed to catch that the system could repeatedly command nose-down trim from erroneous sensor data.
- Who was harmed
- What failed
- Which framework(s) apply
- Which Beacon artifact would have mattered most
- YES-act
- ☐ Ship ☐ Steady ☐ Recover
Case 2 — Knight Capital deployment failure (2012)
A deployment failed to push updated code to one of eight servers. The stale code interpreted new order-routing flags as legacy commands, generating runaway orders that bankrupted the firm in less than an hour.
- Who was harmed
- What failed
- Which framework(s) apply
- Which Beacon artifact would have mattered most
- YES-act
- ☐ Ship ☐ Steady ☐ Recover
Case 3 — Detroit wrongful arrest via facial recognition (2020)
A face-recognition match produced a single candidate; police arrested Robert Williams based on that match alone. No human review, no documented thresholding, no audit trail of the model version or training data.
- Who was harmed
- What failed
- Which framework(s) apply
- Which Beacon artifact would have mattered most
- YES-act
- ☐ Ship ☐ Steady ☐ Recover
Case 4 — Apple Card credit limit bias allegations (2019)
Couples with similar finances saw very different credit limits, including in the same household. Goldman/Apple could not explain the decisions or demonstrate disparate-impact testing.
- Who was harmed
- What failed
- Which framework(s) apply
- Which Beacon artifact would have mattered most
- YES-act
- ☐ Ship ☐ Steady ☐ Recover
Case 5 — Robodebt automated welfare debt scheme (Australia, 2016-2020)
An automated income-averaging algorithm was deployed against welfare recipients without legal authority, generating false debts at scale. Operational decisions were made by automation without documented review or recourse.
- Who was harmed
- What failed
- Which framework(s) apply
- Which Beacon artifact would have mattered most
- YES-act
- ☐ Ship ☐ Steady ☐ Recover
Your case — fill in a real one from your organization
- Incident / system
- Year / date range
- Who was harmed
- What failed
- Which framework(s) apply
- Which Beacon artifact would have mattered most
- YES-act
- ☐ Ship ☐ Steady ☐ Recover
Source: AIGovOps Beacon Auditor Lab Deck (30 slides)
aigovopsfoundation.org · Apache-2.0