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Update AITG-DAT-03_Testing_for_Dataset_Diversity_and_Coverage.md

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Matteo Meucci
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@@ -15,18 +15,18 @@ Dataset Diversity & Coverage testing ensures that AI training and evaluation dat
**1. Demographic and Population Representation Analysis**
Test: Conduct statistical analyses to compare dataset demographic distribution with real-world demographics.
Response Indicating Vulnerability: Significant deviation in demographic representation from the target user population, leading to measurable biases or coverage gaps.
- Test: Conduct statistical analyses to compare dataset demographic distribution with real-world demographics.
- Response Indicating Vulnerability: Significant deviation in demographic representation from the target user population, leading to measurable biases or coverage gaps.
**2. Scenario and Contextual Coverage Test**
Test: Evaluate the dataset for completeness and variety of real-world scenarios relevant to the model’s intended usage.
Response Indicating Vulnerability: Critical real-world scenarios or contexts are inadequately represented or completely missing in the dataset.
- Test: Evaluate the dataset for completeness and variety of real-world scenarios relevant to the model’s intended usage.
- Response Indicating Vulnerability: Critical real-world scenarios or contexts are inadequately represented or completely missing in the dataset.
**3. Bias Detection and Fairness Analysis**
Test: Utilize bias detection tools and fairness metrics (e.g., demographic parity, equal opportunity) on datasets.
Response Indicating Vulnerability: Identification of substantial biases or disproportionate representation affecting certain demographic or contextual groups.
- Test: Utilize bias detection tools and fairness metrics (e.g., demographic parity, equal opportunity) on datasets.
- Response Indicating Vulnerability: Identification of substantial biases or disproportionate representation affecting certain demographic or contextual groups.
### Expected Output