4-Slide Mini Deck · Human Agency Policy Framework
The Policy Framework, in four slides
Micheal Charles Preble · SSRN 7509878 · Policy analysis and proposed framework
Slide 1 of 4
The Problem
Persistent computational systems can preserve the effects of earlier classifications, scores, and decisions after the state that supported them has changed. Correcting the source does not by itself determine whether the consequences it produced should continue.
Slide 2 of 4
The Contribution
Distinguishes correction from revalidation and develops the Consequence Revalidation Framework: Correction → Dependency → Provenance → Participation → Revalidation → Remedy → Closure. Introduces corrective continuity — a valid correction's capacity to remain effective across the pathways superseded state would otherwise keep affecting.
Slide 3 of 4
How It Works · Evidence Status
Grounded in real, cited law: EU AI Act, GDPR, CJEU automated-scoring decisions (SCHUFA, Dun & Bradstreet Austria), and U.S. administrative-reconsideration doctrine.
Evidence status: Policy analysis and proposed framework; no new empirical results.
Slide 4 of 4
Why It Matters · Limits · Open Questions
Correction should trigger revalidation, not automatic reversal. The central asymmetry test: does the architecture preserve old state more effectively than it preserves valid correction?
Limits: not a universal liability rule. Open: whether revalidation duties discourage correction; how participation and confidentiality interact; scale constraints for high-volume systems.
Preble, Micheal Charles. “Human Agency Under Consequential State: A Policy Framework for Correction, Revalidation, and Recoverable Consequences” Manuscript v4, September 2026. SSRN 7509878, submitted to SSRN, 2026.