## 3 Proposed Process for Data Requirements within Requirements Engineering  
We propose a process for incorporating data requirements within requirements engineering for artificial intelligence projects. The typical requirements engineering activities include \"Requirements Elicitation\", \"Requirements Analysis\", \"Requirements Specification\" and \"Requirements Validation\". Our proposed \"Data Requirements\" phase is inserted between the \"Requirements Elicitation\" and \"Requirements Analysis\" activities and consists of \"Legal and Regulatory Data Requirements\", \"Production-like Image Data Acquisition\", \"Data Quality Assurance\", \"Data Labeling\" and \"Label Quality Assurance\" activities. Figure 4 illustrates the general requirements engineering activities with the addition of the data requirements activities. During the requirements elicitation activity, which is the initial phase of understanding the problem that the software must address, it is essential to determine whether a continuous stream of similar data is available and whether these data will be accessible once the project is completed. This consideration is specifically tied to artificial intelligence projects because the performance and effectiveness of machine learning models heavily rely on the availability and quality of data. Ensuring that there is a consistent flow of relevant data for training, validation and testing purposes allows the model to adapt and improve over time, leading to better overall performance and long-term viability of the artificial intelligence solution. The effort required to extract knowledge from the data, whether human or programmatic, must also be assessed. Additionally, it is crucial to identify any issues related to the speed, accuracy or cost of this effort. If a significant investment is being made, the next question to answer is whether the task can be framed as a segmentation, recognition or detection task with a fixed number of predefined classes. Examples of such tasks include detecting abnormalities in MRI scans, classifying traffic lights, detecting cats or creating a visual substitution system for blind people. Following the requirements elicitation activity, the data requirements phase begins. During the legal and regulatory data requirements activity, any relevant regulations are identified and steps are taken to ensure compliance. For example, with medical images, HIPAA and GDPR regulations require patient consent or data de-identification [36]. Once  
Figure 4: Requirements engineering process with data requirements activities for machine learning projects. “QA” stands for “Quality Assurance”. regulatory compliance is understood, production-like image data acquisition can commence. Data should be from the same distribution as the data in production. This means that the data used during the project should closely resemble the real-world data the model will ultimately process when deployed. Further, maintaining a connection to the data source throughout the project's life cycle is crucial. Data sources can include raw camera photos or MRI scans from specific MRI scanners. It is also important to remove any irrelevant data and to ensure a balanced dataset. Data ready for machine learning processes can be stored in commercial storage solutions such as Amazon S3 on Amazon Web Services [37], Azure Blob Storage on Microsoft Azure [38] or Cloud Storage on Google Cloud [39]. Subsequently, data quality assurance is performed by checking structural attributes such as image width, height and pixel histograms and visually inspecting the data's semantics. If any issues with the data are found (e.g., images being too small, too large or containing excessive noise), the production-like image data acquisition activity should be repeated. The frequency of repeating the production-like image data acquisition activity depends on data quality and project requirements.