From early in the product development process, plan for the fact that your AI system will make bad predictions at some point. This is an important part of confirming that AI is the right technology for your project.

Think through the types of errors that your system could make, and their consequences. You should have an informed point of view about what's at stake for your user for a given error and expected impact of false positive and false negative predictions.

Plan to remediate for such errors using approaches like:

• Setting users expectations about your system with explanations
• Providing manual controls when the AI fails
• Offering high-touch customer support

Allow your users to supervise automation and take back control when needed, rather than automating across the board.

This is helpful to users in a number of ways, including:

• Building comfort in new or high stakes situations with the support of controls to override the system if needed.
• Learning how the system works as they exercise their controls. For example, by manually confirming an appointment time recommendation, they become more familiar with the system’s appointment recommendations.
• Giving them a way to complete their task when the system doesn’t work as intended.

Even in cases where users may not frequently exercise the option to take back control, it can be helpful to let them know that they have that option, and to help them build confidence in the system.

When an AI system fails or gives users a poor prediction, the easiest path forward is often to let the user take over in a non-automated way.

Make it as easy and intuitive as possible for users to quickly pick up where the system leaves off. Give them all the information they need to take the reins:

• Awareness of the situation
• What they need to do next
• How to take their next action

In difficult or high-stakes situations, you may even need to redirect users to a human for extra support.