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An Auditable Decision Engine for AI Systems

An Auditable Decision Engine for AI Systems

by adamscottthomas·Mar 3, 2026·3 points·1 comment
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AI Analysis

●MidBig BrainBold Bet

Intriguing cognitive model, but lacks code, demos, or evidence it works beyond theory.

Strengths
  • •Five-phase decision loop with explicit regime arbitration is genuinely creative architecture thinking.
  • •Stress-deformation model (legality graph shrinking under pressure) is a clever formalism for constraint dynamics.
  • •Bypasses-as-feature framing flips the script on safety; refusal is intentional, not failure.
Weaknesses
  • •No runnable code, no experimental validation, no comparison to existing AI safety approaches or baselines.
  • •Landing page is theory-heavy; unclear whether this is a simulation, a framework to build, or architectural philosophy.
Category
AI/ML
Target Audience

AI researchers, autonomous systems designers, safety-focused ML engineers

Similar To

OpenAI's Constitutional AI · Anthropic's RLHF safety frameworks · Berkeley's AI safety research

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