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Which cognitive biases caused Air France's collapse?

5 bias patterns are documented in this case, led by Anchoring Bias. The full set: Availability Heuristic, Anchoring Bias, Groupthink, Authority Bias, Recency Bias. Each is mapped to Decision Intel's 22-bias R²F taxonomy with stable IDs and academic citations.

What is Decision Intel's reasoning audit?

Decision Intel runs a 12-node Recognition-Rigor Framework (R²F) pipeline on strategic memos, detecting cognitive biases from a 22-bias canonical taxonomy and producing a Decision Quality Index (DQI) score. The audit produces a hashed, tamper-evident Decision Provenance Record (DPR) suitable for audit-committee review.

All case studies

Catastrophic failureAerospace2009

Air France

Air France Flight 447 Crash

Estimated impact: 228 lives; €2.9B in litigation

F14

Decision Quality Index (simulated)

Critical · 5 biases detected, 2 toxic combinations

Air France Flight 447 crashed into the Atlantic en route from Rio to Paris, killing 228 people. Pitot tube icing caused unreliable airspeed readings, and the pilots responded with a sustained nose-up input that stalled the aircraft. Despite stall warnings, the crew never diagnosed the stall.

Decision context

Whether pilots adequately diagnosed the automation disconnect and stall condition, and whether Air France's training adequately prepared crews for manual high-altitude flying after automation failure.

Decision anatomy

Monetary stakes

Very high

Dissent suppressed

Time pressure

Present

Unanimous consensus

Decision participants

Dissent encouraged

External advisors

Iterative process

Red = risk factor present · Green = protective factor present

Deep hindsight-stripped analysis coming soon. This case is part of our ongoing backfill to reconstruct what the platform would have flagged at decision time. The summary and bias profile below are drawn from primary sources.

Biases present in the decision

Availability Heuristic

medium

★Anchoring Bias

critical

Groupthink

high

Authority Bias

high

Recency Bias

medium

★ Primary driver · Severity estimated from bias type and decision outcome

3D Decision Knowledge Graph

Air France · 5 biases, 2 toxic combinations

Drag to rotate · Scroll to zoom · Click to explore

Decision

Critical

High

Medium

Toxic

Bias Intensity Profile

Severity × confidence across 5 detected biases

Toxic combinations

!Blind Sprint

Overconfidence combined with planning fallacy creates unstoppable momentum toward failure.

!Echo Chamber

Group members reinforce each other's views, systematically filtering out dissenting signals.

Reference class base rates

Across all 143 curated case studies in our library:

89%

base failure rate

-0%

partial / mixed

11%

base success rate

■ Failure■ Partial■ Success

Lessons learned

Anchoring to automation-era flying skills meant pilots could not revert to basic instrument scan when automation disconnected
Availability heuristic: pilots were trained for engine failures, not high-altitude stalls — the untrained scenario was the fatal one
CRM breakdown: the captain was resting and the two co-pilots failed to communicate their conflicting control inputs

Source: BEA Final Report on AF447 (2012); William Langewiesche, "The Human Factor," Vanity Fair (2014) (NTSB Report)

Pilot slots available

We caught these patterns in Air France's own record — before the outcome.

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Workflows that fire on decisions like Air France’s

The same Recognition-Rigor Framework that documents this case audits memos in the same shape — before the outcome forces the lesson.

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Lessons learned

Anchoring to automation-era flying skills meant pilots could not revert to basic instrument scan when automation disconnected

Availability heuristic: pilots were trained for engine failures, not high-altitude stalls — the untrained scenario was the fatal one

CRM breakdown: the captain was resting and the two co-pilots failed to communicate their conflicting control inputs