# Integrating a Blockchain-Based Governance Framework for Responsible AI
## 1 Introduction
### Responsible AI  
Developing, deploying, and using AI systems in a manner that is ethical and useful to society is what is known as \"responsible AI\", and is a central idea in the study of artificial intelligence (AI) [39; 40]. To make sure AI is utilised for the sake of humanity, we must carefully assess its ethical, legal, and societal consequences. Transparency is a fundamental concept of ethical artificial intelligence. Decision-making processes in AI systems need to be transparent in order to ensure that users and stakeholders can comprehend them [41]. This is especially crucial for decision-making systems in highly confidential fields such as medicine, banking, and law enforcement.  
Fairness is another fundamental element of ethical AI. In order to achieve equality, it is essential that AI systems be built without bias [42; 43]. This is especially crucial for systems used in fields such as employment, finance, and law enforcement [44]. Artificial intelligence systems in these domains should be developed to avoid contributing to the spread of prejudice and bigotry. Data preparation, data augmentation, and model validation are all strategies that may be used to make AI systems more equitable [45]. By employing these methods, it is possible to check whether the data used to train an AI system are indeed representative of the target population and that the model is not being over-fitted to the training data. Fairness in AI systems can be ensured by employing methods such as explainable AI (XAI) [46]. XAI is a collection of methods for increasing the transparency of AI systems, allowing users and other stakeholders to better grasp the reasoning behind the system's actions. This is especially crucial for decision-making systems in highly confidential fields such as medicine, banking, and law enforcement [47; 48].  
As AI systems become more integrated into society, it is crucial that they operate in an ethical and transparent manner. Smart contracts, which are self-executing agreements recorded on a blockchain, have the potential to play a significant role in promoting responsible AI. A taxonomy of the confluence of blockchain for responsible AI is provided in Figure 3. This paper reviews the ways in which smart contracts can establish a framework of rules and standards that ensure ethical and transparent use of AI systems. This includes defining clear limits on how AI can be used as well as creating a system of checks and balances that monitor key performance indicators such as accuracy, bias, and fairness. Smart contracts can provide an audit trail of AI transactions, which helps to ensure accountability and transparency throughout the life-cycle of AI systems. By promoting responsible AI, smart contracts can help to build trust in AI systems and mitigate potential risks associated with their use, ultimately paving the way for more widespread adoption of AI technology.  
The rest of this article is organized as follows. Section 2 covers algorithms, frameworks, and implementation tools for deploying smart contracts with AI. In this section, we analyse network system complexity and energy efficiency. Section 3 examines the verification and authentication systems used to deal with the overall convergence of smart contracts and AI. This section discusses the best pracises in the smart contract review process that verify whether the contract performs as intended and does not include any bugs or vulnerabilities. Towards the mitigation of external cyber-threats, Section 4 discusses security and privacy issues, safe data management, and the implementation of explainability processes into the design of AI systems. The research in this article is conducted through a combination of a literature review and selected case studies.