Verification and validation procedures for artificial intelligence systems involve rigorous evaluation to ensure functionality, reliability, and safety. These processes often encompass examining performance metrics, identifying potential biases, and verifying adherence to specified requirements. For example, evaluating an autonomous vehicle’s performance includes assessing its ability to navigate diverse environments, respond to unexpected obstacles, and adhere to traffic laws.
Comprehensive evaluation is paramount for ensuring AI systems behave predictably and ethically. Historically, inadequate scrutiny has led to unintended consequences, highlighting the need for robust validation methodologies. The ability to rigorously evaluate and improve machine learning models offers advantages such as minimizing potential harms, building public trust, and maximizing the effectiveness of the technology.
