## 7 Discussion  
This article presents an SLR that identifies key ethical themes at the intersection of AI and blockchain, and explores how features of blockchain technology can complement AI in addressing various challenges in current AI systems. As generative AI becomes increasingly pervasive in society, the need for robust ethical frameworks that prioritize human well-being and address concerns such as privacy and bias becomes paramount. Blockchain, with its emphasis on trust, transparency, and decentralized collaboration, can enhance the explainability and accountability of AI systems.  
Our findings suggest a promising but complex landscape characterized by both opportunities and constraints. The analysis presented in this article demonstrates that blockchain can contribute most effectively to AI alignment through three primary avenues: transparency, immutability, and decentralized control. These qualities facilitate auditability and accountability in AI decision-making, which are critical to ensuring alignment with human values. Notably, smart contracts can be leveraged to codify ethical principles grounded in well-established ethical theories, such as [PERSON]'s categorical imperative. Maintaining human control in future AI scenarios is vital, necessitating mechanisms such as Proof of Personhood and human-in-the-loop systems.  
A key finding of this SLR is the importance of ethical considerations at multiple levels: the micro (technical), meso (application), and macro ( societal) levels, all of which are essential for achieving blockchain-based AI alignment. These levels correspond to different stakeholders and systems, ranging from cryptographic primitives and consensus protocols to domain-specific applications (e.g., healthcare, education, and finance), as well as the overarching principles of justice and human dignity.  
Use cases in healthcare, supply chains, education, and humanitarian services illustrate the tangible benefits of integrating blockchain with AI. For example, blockchain ensures data integrity in medical diagnostics, enables consent traceability in clinical trials, and protects sensitive student information in AI-powered educational systems. Across these domains, the blockchain layer serves as critical infrastructure for ethical enforcement and transparency, ultimately enhancing public trust.  
Despite its potential, the integration of blockchain technology into AI alignment frameworks faces significant challenges. The identified limitations of any SLR are related to biases in the availability of publications and the study selection processes, inaccuracy in the study extraction process, and misclassification of published results. Additionally, we have identified the following challenges that provide exciting opportunities for future work: Our review reveals a gap in the literature, as much of the existing research overlooks or fails to address the emerging ethical implications of blockchain, highlighting an urgent need for comprehensive ethical frameworks and standards that address critical concerns such as accountability, transparency, and interoperability. On the technical side, issues like scalability and privacy risks demand the use of advanced cryptographic techniques and careful design. Although practical implementations demonstrate the potential of blockchain in supporting AI alignment, they also underscore the importance of overcoming existing barriers, as this is needed to further enable the development of ethical blockchain-based AI systems.