Pattern 11F: Example-based explanations

Problem

The user needs an explanation for why the system did what it did and an explanation for a single instance may not be sufficient.

Solution

Provide one or multiple examples of instances similar to the user’s input and their corresponding explanations.

Use when

  • It is more efficient for the user to understand the AI system’s behavior from examples.
  • A full explanation is too complex to communicate efficiently.
  • Often useful for systems dealing with images, audio, or video.
  • The user doesn’t understand why the system took a specific action.
  • The user wants to understand the system’s logic.
  • Policy or regulations require the system to make an explanation available.

How

Collaborate with an AI/ML practitioner to collect information about how similar examples to the user’s input can be extracted from the system’s past by:

  • Defining criteria for similarity.
  • Retrieving top examples similar to the user’s input, based on similarity criteria.

When selecting what examples to display, consider:

  • Which dimension(s) of similarity between examples and user input to illustrate.
  • Including diverse examples to illustrate multiple dimensions of similarity, avoiding redundancy.

Provide an initial set of examples and enable the user to access additional ones on demand.

User benefits

  • Facilitates user understanding by showing rather than telling.
  • Enables the user to understand specific AI system decisions.
  • Builds user trust in the system.
  • Enables the user to quickly understand the system’s reasoning.
  • Understanding the system’s reasoning in turn enables the user to predict the system’s behavior and troubleshoot when the system’s behavior is undesirable.

Common pitfalls

  • Criteria for similarity are not meaningful to users.Providing not enough or too many examples for the user’s context.

References

Examples

Guideline 11 > Pattern 11F > Example
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Guideline 11 > Pattern 11F > Example
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