When your AI-enabled system behaves in a way that a user doesn’t expect or want, make sure that they have an option to share feedback. And, as much as possible, use that feedback to improve your model.

Feedback in AI systems can take a range of forms, including,

• Giving a thumbs up or thumbs down on a recommendation
• Hiding unwanted recommendations
• Flagging or reporting problematic recommendations
• More traditional feedback flows, where a user manually reports a problem through a form or other mechanism

Once a user gives feedback, acknowledge that you received it. If possible, let them know how the system will respond to the feedback.

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 determining how much to automate your product flows, think about the stakes of your product, and the level of comfort that users may have with your type of product.

In low risk, well-established products, like content recommendation systems, you might choose to prioritize a more heavily automated product flow where user control is available but optional.

However, when onboarding to a new type of product, or in high-stakes situations, errors can be particularly problematic, and can corrode user trust and potentially cause dangerous situations. In such cases, design your system to give users more control over the system.

As you design your product, think critically about the balance of automation and control that you need to offer your users for them to use your product successfully.

In some cases, it may make sense to offer multiple “levels” of automation in your product (for example, partial automation and full automation), to help users with varying levels of comfort or familiarity with your product use it successfully.

In such cases, start users with the lowest level of automation, and progressively increase automation. Make sure that users can easily adjust their choice, including dialing up or down their level of automation, and that the steps between levels are small and land well.

Choosing the right level of automation depends heavily on your user and product needs and context. Research from Sheridan et al has demonstrated 10 levels of automation to consider.

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.