# Integrating a Blockchain-Based Governance Framework for Responsible AI
## 3 System Architecture and Information Flow  
The use case in the previous section illustrates how smart contracts can be used to enforce certain responsible AI practices and make AI systems' decision-making processes more transparent and auditable, as depicted in Figure 4. It should be noted that this is a simplified example; a real-life use case would be more complex, and would require additional measures such as access control, encryption, and compliance with regulations such as the GDPR. The conventional method consists of aggregating data from several source systems, cleaning, and reconciling errors, and then loading the data into a single business data warehouse [99]. The scope of an institution's central data repository may vary. Individual apps that own the business process continue to be the holy grail of authenticity. Many organisations have several data warehouses (e.g., for marketing and compliance), whereas others develop specialised data marts from a core warehouse [100]. Consequently, tracing a data discrepancy back to the system that owns that data is, to say the least, a complicated endeavour. Integrating smart contracts with the information flow, as depicted in Figure 4, brings two fundamental changes:  
1. First, all business actions result in predictable changes to the underlying data. This means that we may now see important entities as \"digital assets\" on which actions are performed. A credit card provided to a consumer, for instance, is a digital asset on which a fraud flag is raised, a payment is made, a campaign is conducted, etc. 2. Second, instead of being concealed in various flowcharts and paperwork, a company's whole business process is now embodied in tangible form. Any corporate IT professional is aware that documentation becomes obsolete as soon as it is created. With smart contracts, your business process is simply codified into functional software, which is then maintained and managed in accordance with the organisational hierarchy. The smart contracts store additionally provides a single source of truth for the applications landscape built on top of smart contracts. These data are produced in real time by operational procedures, meaning that there is no need to perform any post-execution reconciliation or aggregation. It is inevitable that considerable effort is invested on data staging, reconciliation, and aggregation, i.e., ELT (Extract, Load, Transform). The larger the organisation, the greater the number of source systems and application silos, and the more complex the total data management architecture. The complexity of integrating new services presents obstacles for corporate innovation. Data warehousing can be integrated with smart contracts via either the ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) approach [101]. In traditional data warehousing, ETL is used to process and store data from various sources into a centralized location for reporting and analysis. In this process, data are extracted from various sources, transformed to fit the scheme of the data warehouse, and then loaded into the warehouse for storage [102]. In contrast, ELT is a variation of ETL in which data are first loaded into the data warehouse in raw form and then transformed. This allows for the data warehouse to be more flexible, as the data can be transformed on-the-fly as needed, rather than requiring  
Figure 4: Flowchart of a smart contract-based Responsible AI setup with ETL for business intelligence and analytics (ETL: Extract, Transform, and Load). preprocessing prior to loading. This comparison between ETL and ELT is summarised in Table 1.  
ELT can be beneficial in the smart contract context, as the data stored in a smart contract are immutable and cannot be altered. This means that data must be transformed before being stored in the smart contract; using ELT allows for data to be transformed on-the-fly as they are loaded into the smart contract.