About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Impact score evaluation metric
Last updated: Feb 21, 2025
The impact score evaluation metric compares the rate that monitored groups are selected to receive favorable outcomes to the rate that reference groups are selected to receive favorable outcomes.
Metric details
Impact score is a fairness evaluation metric that can help determine whether your asset produces biased outcomes.
Scope
The impact score metric evaluates generative AI assets and machine learning models.
- Types of AI assets:
- Prompt templates
- Machine learning models
- Generative AI tasks: Text classification
- Machine learning problem type: Binary classification
Scores and values
The impact score metric score indicates whether monitored groups receive higher rates of selection than reference groups. Higher scores indicate higher selection rates for monitored groups.
- Range of values: 0.0-1.0
- Best possible score: 0.0
Settings
- Thresholds:
- Lower limit: 0.8
- Upper limit: 1.0
Do the math
The following formula calculates the selection rate for each group:
The following formula calculates the impact score:
Parent topic: Evaluation metrics
Was the topic helpful?
0/1000