Overview

For data collection involving human subjects, why, how and by whom data is being collected should be established in the Pre-Design stage. Potential data challenges or data bias issues that have implications for diversity and inclusion should be identified by key stakeholders and data scientists. For example, in the health application domain, diverse data sources ensuring equitable AI should be identified and collected. Data sources used in primary care decision-making must not only reflect clinical data but also incorporate social determinants of health (that is, where patients are born, grow, work, live, and age). Project teams should develop mitigation and monitoring strategies to counter data issues. All such information should be captured systematically and reviewed regularly.