Update frameworks/sample/framework.json

frameworks/sample/framework.json

@@ -171,7 +171,7 @@
       "description": "The organization shall ensure that data collection and preprocessing steps are designed to minimize the introduction of biases and ensure data quality. The following measures shall be implemented:\n\na. Data Source Selection: Identify and select diverse and representative data sources to reduce the risk of biases arising from limited or skewed data.\n\nb. Data Sampling Techniques: Employ appropriate data sampling techniques, such as stratified sampling or oversampling, to ensure balanced representation of different groups or classes in the training data.\n\nc. Data Quality Checks: Implement data quality checks to identify and address issues such as missing values, outliers, inconsistencies, and errors in the collected data.\n\nd. Data Preprocessing Guidelines: Establish and follow guidelines for data preprocessing tasks, including data cleaning, normalization, and feature selection, to maintain data integrity and reduce the introduction of biases.\n\ne. Data Labeling and Annotation: Ensure that data labeling and annotation processes are performed consistently and objectively, with clear guidelines and quality control measures to minimize the introduction of biases.\n\nf. Data Documentation: Maintain comprehensive documentation of data collection and preprocessing steps, including data sources, sampling methods, preprocessing techniques, and any assumptions made during the process.",
       "controlCategory": "Data Bias",
       "readableControlId": "AIDBA-2",
-      "severity": "medium",
+      "severity": "low",
       "automationPlatforms": [],
       "criteria": [
         {
@@ -212,7 +212,7 @@
       "description": "The organization shall ensure that the model development and training phase incorporates measures to mitigate biases and promote fairness. The following measures shall be implemented:\n\na. Algorithm Selection: Select appropriate algorithms that are less prone to amplifying biases present in the training data. Consider the use of fairness-aware algorithms or algorithms with built-in bias mitigation techniques.\n\nb. Hyperparameter Tuning: Conduct thorough hyperparameter tuning to optimize model performance while considering fairness metrics. Evaluate the impact of different hyperparameter settings on bias mitigation.\n\nc. Bias Mitigation Techniques: Incorporate bias mitigation techniques during model training, such as regularization, adversarial debiasing, or fairness constraints. Document the specific techniques applied and their effectiveness in reducing biases.\n\nd. Training Data Balancing: Ensure that the training data is balanced and representative of the target population. Apply techniques like resampling, oversampling, or undersampling to address class imbalances that may contribute to biases.\n\ne. Fairness Metrics: Incorporate fairness metrics during model training and validation to assess the model's performance in terms of fairness and bias mitigation. Use appropriate fairness metrics based on the specific context and requirements of the AI system.\n\nf. Model Transparency: Maintain transparency in the model development process by documenting the algorithms used, hyperparameter settings, bias mitigation techniques applied, and any assumptions made during training.",
       "controlCategory": "Data Bias",
       "readableControlId": "AIDBA-3",
-      "severity": "medium",
+      "severity": "high",
       "automationPlatforms": [],
       "criteria": [
         {
@@ -253,7 +253,7 @@
       "description": "The organization shall conduct comprehensive evaluations and testing of trained AI models to assess their performance, fairness, and the presence of any residual biases. The following measures shall be implemented:\n\na. Evaluation Metrics Selection: Select appropriate evaluation metrics that cover both performance and fairness aspects of the AI model. Include metrics such as accuracy, precision, recall, F1-score, as well as fairness metrics like demographic parity, equalized odds, or equal opportunity.\n\nb. Testing Methodology: Establish a robust testing methodology that includes techniques such as cross-validation, holdout validation, or stratified sampling to assess the model's performance and fairness across different subsets of the data.\n\nc. Bias Testing: Conduct targeted bias testing to evaluate the model's performance across different protected attributes or sensitive groups. Assess the model's fairness and identify any disparities in outcomes or error rates across these groups.\n\nd. Threshold Analysis: Perform threshold analysis to determine the appropriate decision thresholds for the AI model, considering both performance and fairness metrics. Evaluate the impact of different threshold settings on the model's fairness and accuracy.\n\ne. Residual Bias Assessment: Assess the presence of any residual biases in the trained model that may not have been fully mitigated during the training phase. Identify the sources and magnitude of these biases and develop strategies for further mitigation.\n\nf. Model Validation: Validate the AI model's performance and fairness on independent test datasets that were not used during training. Ensure that the model generalizes well to unseen data and maintains its fairness properties.",
       "controlCategory": "Data Bias",
       "readableControlId": "AIDBA-4",
-      "severity": "medium",
+      "severity": "high",
       "automationPlatforms": [],
       "criteria": [
         {