RAIRC03-BP08 Measure explainability of system behavior
Consider metrics for explainability based on user studies that quantitatively measure stakeholders' ability to understand system outputs, including their comprehension of confidence scores, reasoning paths, and limitations, while also tracking the effectiveness of provided explanations across different user groups and expertise levels. This can include objective metrics (such as task completion rates when acting on AI explanations) and subjective assessments (like user satisfaction scores and trust ratings). Pay particular attention to whether users can accurately identify when to rely on or question the system's outputs.
Level of risk exposed if this best practice is not established: High
Implementation considerations
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Create baseline measurement approaches that check whether users can correctly interpret what your system is telling them and why. Include the user groups from your RAIBR02 risk assessment who need to understand system outputs, and design simple comprehension tests for confidence scores, reasoning paths, and system limitations.
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Design objective testing that measures how successfully users complete tasks when they rely on your system's explanations. Build tests that track task completion rates, decision accuracy, and time to completion when users act on AI explanations and when they work without them. Test across different expertise levels to see where your explanations assist users to make better decisions and where they might mislead people.
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Build subjective assessment tools that capture user satisfaction, trust levels, and confidence in your system's explanations. Create simple rating scales and feedback collection methods that show whether users feel your explanations are helpful, trustworthy, and simple to understand. Track how these subjective measures vary across different user groups so you can spot where your explanations work well and where they fall short.
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Test whether users can accurately judge when to trust or question your system's outputs by creating scenarios where the system should and shouldn't be trusted. Build measurement approaches that check if users correctly identify high confidence as compared to low confidence situations and whether they appropriately rely on or override system recommendations. This testing assists you to catch cases where users might over- or under-trust your system.
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