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Escaping the Agreement Trap: Defensibility Signals for Evaluating Rule-Governed AI explores A new evaluation framework for rule-governed AI that measures policy-grounded correctness, moving beyond simple agreement metrics.. Commercial viability score: 6/10 in AI Governance.
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Canonical ID escaping-the-agreement-trap-defensibility-signals-for-evaluating-rule-governed-ai | Route /paper/escaping-the-agreement-trap-defensibility-signals-for-evaluating-rule-governed-ai
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/buildability/escaping-the-agreement-trap-defensibility-signals-for-evaluating-rule-governed-ai
Subject: Escaping the Agreement Trap: Defensibility Signals for Evaluating Rule-Governed AI
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Dimensions overall score 6.0
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Receipt path
/buildability/escaping-the-agreement-trap-defensibility-signals-for-evaluating-rule-governed-ai
Paper ref
escaping-the-agreement-trap-defensibility-signals-for-evaluating-rule-governed-ai
arXiv id
2604.20972
Generated at
2026-04-24T20:30:22.591Z
Evidence freshness
fresh
Last verification
2026-04-24T20:30:22.591Z
Sources
3
References
0
Coverage
50%
Lineage hash
92a8fb00b6e30898904cbc07a9f9204ddfdd0866c60789b57e09f155c2e397f3
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This equation captures one of the core mathematical components of the system. Ma alongside content C and proposed decision ˆy. Ma generates trace A = (κ, ω, ι, ξ) and classifies
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This equation captures one of the core mathematical components of the system. fensibility labels (yi = 1 if L1/L2, yi = 0 if L3), using softmax reparameterization (α, β, γ) =
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This equation captures one of the core mathematical components of the system. the L2/L3 distinction. Consistency of β∗≈0 across two independent operationalizations (H[w] and
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