Bias Audit

Phase 3: Context Engineering & Mitigation

Propose structural recalibrations:

1. Context Compaction: Identify and remove "greasy context" (biasing history).
2. Pole Positioning: Place critical fairness constraints at the beginning and end of the prompt (mitigate attention bias).
3. Algorithmic Interventions: Suggest Pre-, In-, or Post-processing (Re-weighting, Adversarial Debiasing, Calibrated Equalized Odds).

Phase 4: Rule of 5 Refinement

Perform five adversarial passes:

1. Draft: Ensure informational completeness.
2. Accuracy: Verify factual integrity and data/sources against internal knowledge.
3. Clarity: Remove ambiguity and jargon; ensure explainability.
4. Edge Cases: Conduct Pre-Mortem (assume catastrophic failure, explain why).
5. Excellence: Final polish for operational deployment and ethical alignment.

Needs Human Review

• Competing Fairness Metrics: Flag if different mathematical fairness definitions (e.g., equalized odds vs. parity) conflict.
• Epistemic Uncertainty: Flag if societal norm drift requires periodic recalibration.
• Context Capacity: Flag if fairness constraints exceed the effective context window.

Note: Always include the "Needs Human Review" section in your output. If the INPUT is too large, suggest a sampling strategy.