TrustChain-Review: A Risk-Adaptive Blockchain and Game-Theoretic Framework for Trustworthy AI-Assisted Code Review

Blockchain evidence improves accountability in AI code review: The framework's full-evidence configuration, which uses blockchain to create verifiable records of code changes and review decisions, produced the strongest reputation accuracy and trust convergence results, addressing the paper's core concern that "centralized reputation records may be difficult to audit."

Risk-adaptive governance balances security with efficiency: Rather than applying maximum oversight to all code reviews, the framework selectively increases governance controls based on risk level. This approach reduced governance costs while maintaining reasonable detection of malicious and superficial reviews, suggesting that responsible AI oversight doesn't require uniform, resource-intensive monitoring of every decision.

Game-theoretic incentives can align stakeholder behavior: By modeling developers, reviewers, and platforms as strategic players with competing interests, the framework demonstrates that financial and reputational incentives can be designed to discourage both developers from submitting unverified code and reviewers from approving changes without proper inspection—two key accountability gaps in AI-assisted development.

Cost-efficiency matters for practical implementation: The study found that while full-evidence governance produced the best results, the risk-adaptive configuration achieved better "cost-efficiency" by applying stronger controls selectively. This suggests that responsible AI governance frameworks must consider implementation costs to be actually adopted in real-world software development environments.

High-risk scenarios justify stronger oversight: The paper concludes that "strong evidence-based governance is most appropriate for high-risk" contexts, implying that responsible AI deployment should match governance intensity to the potential consequences of failures rather than applying one-size-fits-all policies.