Post by Journal of Computational Law and Legal Technology

🧠 AI model accuracy is no longer the bottleneck—regulatory audits fail because decision evidence can’t be reliably reconstructed. In a new article published in the Journal of Computational Law and Legal Technology, Dr. Ian T. Staley shows that many AI‑driven FinTech deployments fail supervisory reviews not due to underperformance, but because decision processes cannot be defensibly replayed over time. ⚙️ Using a document‑driven control‑mapping methodology based on NIST AI RMF and the EU AI Act, the study introduces: ✅ A 12‑control evidence taxonomy across decision traceability, integrity, audit replay, and retention governance   ✅ A Minimum Viable Evidence Layer (MVEL) that bundles lineage, rationale, and human actions into replayable evidence packages   ✅ A comparative evaluation of centralized vs. blockchain‑anchored evidence architectures Selective blockchain anchoring provides material incremental compliance value—but only for integrity‑critical artifacts (approvals, version provenance, attestations). The rest remains in conventional enterprise stacks. 📖 The paper reframes AI governance in finance as an evidence system design problem, moving beyond model‑centric controls toward audit‑ready decision replay. 🔗 Read the article and explore the full MVEL architecture through https://lnkd.in/e9JD5b4b. #AIGovernance #FinTech #RegulatoryCompliance #LegalTechnology #ComputationalLaw