ROOTFALL: Executable Independent-Corroboration Runtime for AI Decisions Under Counterfactual Root Ablation
Year: 2026
Type: article
Abstract: High-stakes automated decisions increasingly rely on multiple apparently independent analyses — credit-scoring committees, intelligence fusion systems, regulatory compliance checks — treating agreement among several sources as corroborative evidence. When those sources secretly share vendor data feeds, identical underlying models, or common preprocessing pipelines, this agreement constitutes false plurality: the illusion of independent corroboration from structurally dependent roots. Existing explainability tools such as SHAP and LIME attribute feature importance within a single model but do not test whether multiple decision paths share hidden evidentiary roots that would collapse under ablation. This paper introduces ROOTFALL, an executable independent-corroboration runtime that detects and prevents false plurality in multi-source AI decisions. The architecture implements a six-stage pipeline: (1) decision-and-action capsule freezing that captures the complete decision context before any modification; (2) conservative root clustering using provenance-adjacency graphs to identify shared evidentiary origins across decision paths; (3) counterfactual root ablation that systematically removes each root and its descendants to test decision stability; (4) necessity and sufficiency scoring that quantifies how much surviving support remains after ablation; (5) action-bound certificate generation that binds independence evidence to specific executable decisions; and (6) a fail-closed execution gateway that blocks action when certified independence does not survive ablation. The key insight is that corroboration must be tested structurally — by ablating shared roots and observing whether the decision survives — rather than assumed from surface-level agreement among sources that may share hidden common ancestry. A credit committee where three analysts use the same Bloomberg terminal feed, or an intelligence fusion system where multiple reports derive from a single intercept, can produce unanimous agreement that collapses entirely when the shared root is removed. This Public Research Edition (v2.3.0) includes a proof-of-concept implementation with a passing Reality Gate demonstrator (7/7 core tests), benchmark evidence, mutation testing at or above 90 percent, alternative implementation replication, a credit-committee false-plurality scenario with nineteen decision paths, and stress tests scaling to 1,000 paths and 500 roots. Standards adjacency covers EU AI Act Articles 13 and 14 transparency requirements with honest assessment levels. ROOTFALL does not replace regulated credit models and does not claim production GRC integration. Independence thresholds remain heuristic pending calibration on labeled corpora. This independent technical blueprint does not claim peer review, independent human replication, patentability, or legal compliance opinion. Real-Invention Readiness is assessed at approximately 95%, capped pending independent replication. All claims should be treated as blueprint-grade until independently replicated. Released for evaluation, citation, and attempted falsification of the false-plurality detection claim.
Author: Haxhijaha, Agim
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7169016190
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