FL – 004 | The Cognitive Breach Audit

LOG ID: FL-004
CLASSIFICATION: AI-Driven Algorithms
SECURITY STATUS: Active / Behavioral Audit
SUBJECT UNDER AUDIT: Algorithmic Bias / Cognitive / Attention Optimization
PRIMARY AUDITOR: Paul Mindra (AI Integrity Auditor)

Purpose

Detect algorithmic manipulation and attention engineering that distort beliefs and create divergent realities.

Immediate actions

• Collect representative feed samples for affected cohorts.
• Pause any amplification signals you control for the suspected narrative.

Step‑by‑step checks

• Map ranking signals: identify features (engagement, recency, personalization) driving distribution.
• Collect multi‑segment snapshots: capture feeds for different user profiles and time windows.
• Measure content drift: track topic and sentiment shifts after algorithmic boosts.
• Test feedback loops: inject benign signals and observe amplification behavior.
• Check user controls: verify opt‑out options and transparency mechanisms.
• Correlate external events: compare narrative spikes with platform changes or coordinated campaigns.

Evidence to collect

• Feed snapshots; recommendation logs; A/B test records; model feature lists; timestamps.

High‑confidence red flags

• Opaque ranking criteria; persistent exposure to a single narrative despite reputable contradictions; rapid sentiment shifts tied to algorithm changes.

Action thresholds

• High risk: throttle signals, add human review gates, notify stakeholders.
• Medium risk: increase transparency and user controls.
• Low risk: monitor and document.

Phone script

“I’m pausing the amplification and collecting feed samples; I’ll update you after a quick review.”

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