# Integrating a Blockchain-Based Governance Framework for Responsible AI
## 2 Programming Smart Contracts for Responsible AI
### System Integration and Complexity  
The aforementioned algorithms can be integrated into smart contracts and automatically enforced to prevent bias and ensure accountability in AI decision-making. However, it is important to note that these algorithms are not silver bullets and may not be suitable for every use case. The mostappropriate algorithm depends on the specific requirements of the AI system in question.  
The real-world integration of these algorithms depends on their flexibility, scalability, and seamless integration with the existing hardware [77]. The hardware complexity is dependent on the specific algorithm and the size of the dataset being processed. Fairness algorithms can be computationally intensive, particularly when working with large datasets. For example, the ZVG algorithm [70] and the Prejudice Remover algorithm [71] both require multiple passes over the dataset, which can be time-consuming. However, these algorithms can be implemented on standard hardware and can be optimized for distributed computing environments. XAI algorithms can be computationally intensive, particularly when working with large and complex models. For example, although the LIME and SHAP algorithms can be computationally expensive [73], they can be optimized for distributed computing environments.  
Auditing algorithms can be computationally less intensive than LIME and SHAP, as they require monitoring and evaluating the performance of an AI model over time. The FAA algorithm can be run periodically, and does not need to be run on real-time data. Decentralized AI algorithms can be computationally intensive, particularly when working with large datasets and complex models. While the Federated Learning and MPC algorithms can require significant computational resources, they can be optimized for distributed computing environments. In general, the hardware complexity of these algorithms can be managed by using distributed computing (sharding) and cloud-based resources (off-chain processing) [78]. Additionally, hardware acceleration technologies such as GPUs can be used to speed up the processing time of certain algorithms.  
One of the main benefits of using a distributed network for smart contract execution is increased security. By distributing the contract across multiple nodes, it becomes much more difficult for a malicious actor to tamper with or disrupt the execution of the contract. Additionally, distributed networks are often more resistant to outages and other forms of downtime, ensuring that the contract continues to execute as expected [79]. Another benefit of distributed computing is transparency. Smart contracts are stored on a public blockchain, which allows for anyone to view and verify the terms and execution of the contract. This transparency can help to build trust and confidence in the contract, as all parties can see that it is being executed as intended [80].  
Another benefit of using cloud-based resources is scalability. Cloud providers offer a wide range of computing and storage resources that can be easily accessed and scaled, allowing for the efficient execution of smart contracts and the ability to handle large amounts of data [81]. This is particularly useful for decentralized applications that are expected to handle a large number of transactions or data. Finally, cloud-based resources can provide cost savings for smart contract deployment and execution. Instead of having to invest in and maintain their own infrastructure, organizations can use the resources provided by a cloud provider on a pay-as-you-go basis, which can be much more cost-effective [82; 83; 84].  
In summary, distributed computing and cloud-based resources can benefit smart contracts by providing increased security, transparency, scalability, and cost savings for their deployment and execution.