How would you ensure data quality in an AI system during the design phase?

Ensuring data quality starts in the design phase by implementing several key practices: First, establish clear data quality metrics that align with the objectives of the AI system. Use data validation checks at the point of data collection and preprocessing to identify inconsistencies or errors. Incorporate methods for data cleaning and normalization to maintain uniformity. Additionally, consider implementing training and maintaining specific quality control processes, like human reviews on sample datasets, regularly updating data sources to reflect the most accurate and relevant information. Lastly, develop a feedback loop to continuously monitor and improve data quality through regular audits and performance assessments.