AI Quality is determined by evaluating an AI system’s performance, societal impact, operational compatibility, and data quality. Performance is measured by the accuracy and generalization of the AI model’s predictions, along with its robustness, fairness, and privacy. Societal impact considers ethical implications, including bias and fairness. Operational compatibility ensures the AI system integrates well within its environment, and data quality is critical for the model’s predictive power and reliability.

• Manufacturing industry
• Healthcare
• Supply chain management
• Customer service
• Automotive
• Financial services
• Telecommunications

A company uses an AI-powered quality control system to monitor and analyze the production process of a manufacturing plant. The system tracks and evaluates the quality of raw materials, detects anomalies in the production process, and provides real-time feedback to operators to improve efficiency and reduce waste.

• Data Quality Improvement Process