Data Collection and Operational Consistency
Albert J. Marcella · 2025
Focusing on the processes involved in auditing data collection and ensuring operational consistency in artificial intelligence (AI) systems, Chapter 24 examines the importance of evaluating data quality, identifying biases, and verifying that data aligns with ethical and regulatory standards. The chapter explores key aspects, such as data preprocessing, operational reliability, and compliance with privacy regulations, ensuring that AI systems function predictably and fairly. Procedures for continuous monitoring and improvement are detailed, along with methods for addressing data security and stakeholder feedback. Audit questions are included to assess data reliability, consistency, and representativeness, ensuring the integrity of AI systems from development to deployment.