What action should be taken if an AI system produces unexpected outputs?

When an AI system produces unexpected outputs, treating it as an incident and following the established plan is essential for several reasons. First, unexpected outputs can indicate underlying issues within the AI system, such as biases in the training data, flaws in the algorithm, or environmental factors that have not been accounted for. Recognizing this as an incident allows for a structured approach to investigate and understand the cause of the unexpected behavior.

Furthermore, following a predefined incident management plan helps in maintaining compliance with governance, risk management, and quality assurance protocols. This ensures that all necessary steps are taken to assess the impact of the unexpected outputs, manage risks associated with them, and implement corrective actions. By treating the situation as an incident, organizations uphold accountability and transparency, which are crucial in AI governance.

In contrast, ignoring unexpected outputs can lead to unaddressed problems that may worsen over time. Documenting the outputs for future review is undoubtedly important but is better utilized as part of the incident management process rather than a standalone action. Engaging users for feedback can provide valuable insights but may not be sufficient if an immediate response and investigation are required. It’s essential to prioritize a structured incident response to efficiently handle the situation.