## 7 Discussion
### Research Agenda  
Studies that focus on micro-level concerns generally examine how blockchain core features, such as consensus protocols, smart contracts, or data privacy methods, can enhance AI systems on a technical or organizational basis. At the meso level, different domains are examined. However, studies focusing on this level of analysis provide examples of blockchain, AI, and ethics, but rarely situate their findings in a wider integrative context, inparticular, the context that could inform multi-domain applications or facilitate cross-sector interoperability. Studies that adopt a macro-level view highlighting themes like regulatory policy, societal acceptance, or overarching ethics theories often do not specify how to translate them into implementable global standards and frameworks. Consequently, there is a gap in the literature when it comes to designing and validating holistic frameworks that can connect micro-level technical architectures, meso-level domain-specific requirements, and macro-level policies in a unified way. The existing research rarely explains how micro-level tools, such as Zero-Knowledge Proofs, blockchain consensus mechanisms, or token-based governance, can be seamlessly linked to macro-level requirements, such as sustainability, human rights, and ethics. This is especially true in cross-sector AI applications where stakeholders may have heterogeneous goals and legal obligations. Moreover, only a few studies propose criteria or metrics for a systematic assessment of blockchain-based AI alignment. This leaves open questions on how to measure effectiveness, assess risks, and adapt to evolving use cases and regulations. To close this gap, we have identified and outline here the following research directions:  
1. _Multi-Layered Framework Development_: Integrated models should be developed that incorporate micro-level security and privacy mechanisms, meso-level sectoral governance, and macro-level legal constraints into a single, adaptable structure. Researchers could develop prototype architectures in which smart contracts or token systems reflect high-level ethical rules while maintaining sector-specific standards. 2. _Cross-Sector Interoperability:_ Future studies could systematically compare how blockchain-based AI systems work in sectors as diverse as healthcare and financial services to derive cross-sector best practices. This comparative perspective should clarify how universal ethical guidelines can be mapped to localized compliance requirements to enable cross-sector interoperability of technology and governance. 3. _Metrics and Evaluation for AI Alignment:_ Standardized metrics are needed to assess whether blockchain-based AI solutions meet ethical standards regarding, for example, privacy and fairness. Future work could investigate quantifiable indicators such as bias detection rates or transparency indices and evaluate them empirically in various pilot projects and industrial use cases. 4. _Adaptive Governance and Lifecycle Management:_ Another gap in the literature concerns the continuous monitoring of blockchain-based AI systems, where ethical requirements evolve in parallel with changing regulations or application-specific constraints. Research on adoptive governance mechanisms, smart contract updates, and automated monitoring would contribute to ensuring that, once deployed, solutions remain compatible with evolving societal expectations. 5. _Scalability and Sustainability:_ Only a few papers address the environmental footprint or scalability bottlenecks of emerging technologies such as blockchain and AI. Future work should therefore discuss efficient consensus algorithms, off-chain data approaches, and hardware optimizations so that blockchain-based AI alignment does not become too resource-intensive.