Academic Publication
Presented at XI International Scientific Conference “High Technologies. Business. Society”, Borovets, 23–26 March 2026.
Journal: Artificial Intelligence Proceedings, 2026 • ISSN: 3033-2923 (Print) / 3134-1667 (Online)
Abstract
This paper proposes a conceptual methodological framework based on a Dual-Domain Architecture mediated by a Zero-Knowledge Audit Proxy (ZKAP) to reconcile AI Act accountability with GDPR data minimization. Legal norms are polynomialized into R1CS constraints, transforming compliance into a formally verifiable computational property.
For cognitively opaque exascale models, these invariants may be hardware-anchored through a Provable Arithmetic Logic Unit (pALU), ensuring determinism and resistance to algorithmic drift. For lower-risk or on-premise systems, ZKAP operates in a software-only configuration, enabling periodic asymmetric regulatory proofs without silicon-level integration.
A calibrated threshold distinguishes admissible technical variance from structural divergence, triggering mandatory safeguards. The framework provides a proportional, scalable, and cryptographically verifiable oversight model applicable both to future non-explainable AI systems and to lighter local infrastructures.
Keywords
ZKAP, pALU, Polynomialization of Law, R1CS, Safety-by-Design, Dual-Domain Architecture, Cryptographically Anchored Verification.
Full Text & Related Publications
Full Paper:
Radoslavov (2026) — Full Paper →
Related Publication [1]:
Radoslavov, R.Y. (2026). “Management and Regulation of Artificial Intelligence Models: Concept for Transparency and Accountability in Administrative Activities.” Industry 4.0, Vol. 2, pp. 320–321, ISSN 2534-997X.