A synergistic governance framework for algorithmic transparency obligations and intellectual property protection
Mohong Liu · Advances in Social Behavior Research · 2025
Regulatory initiatives on algorithmic accountability now oblige organisations to reveal decision logic, data provenance, and safety controls, yet the same systems embody proprietary assets whose exposure can nullify competitive advantage. This study engineers and empirically validates a governance framework that reconciles those apparently contradictory imperatives. A three-tier disclosure architecture is combined with lattice-based cryptographic watermarking and policy-driven smart-contract gates, then exercised in two high-stakes domains: a regulated credit-scoring engine and a 1.1-billion-parameter text-to-video generator. Across 18,000 simulated disclosure transactions, the framework achieves a statutory-coverage score of 0.927 0.018 while suppressing parameter-exfiltration entropy to 1.37 bits kg, a 63.8 % reduction relative to a full-disclosure baseline. Median inference latency rises only 3.6 ms, and predictive accuracy remains statistically unchanged (AUC = 0.0007, p = 0.746; FID = 0.09, p = 0.532). Sensitivity analyses confirm that compliance quality varies by less than 2.4 % under 20 % weight perturbations, evidencing robustness. Findings demonstrate that calibrated transparency and vigorous IP protection are jointly attainable, providing quantitative benchmarks for emerging legislation and standardisation efforts.