Bias Drift Violations - Amazon SageMaker AI
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Bias Drift Violations

Bias drift jobs evaluate the baseline constraints provided by the baseline configuration against the analysis results of current MonitoringExecution. If violations are detected, the job lists them to the constraint_violations.json file in the execution output location, and marks the execution status as Interpret results.

Here is the schema of the bias drift violations file.

  • facet – The name of the facet, provided by the monitoring job analysis configuration facet name_or_index.

  • facet_value – The value of the facet, provided by the monitoring job analysis configuration facet value_or_threshold.

  • metric_name – The short name of the bias metric. For example, "CI" for class imbalance. See Pre-training Bias Metrics for the short names of each of the pre-training bias metrics and Post-training Data and Model Bias Metrics for the short names of each of the post-training bias metrics.

  • constraint_check_type – The type of violation monitored. Currently only bias_drift_check is supported.

  • description – A descriptive message to explain the violation.

{
    "version": "1.0",
    "violations": [{
        "facet": "string",
        "facet_value": "string",
        "metric_name": "string",
        "constraint_check_type": "string",
        "description": "string"
    }]
}

A bias metric is used to measure the level of equality in a distribution. A value close to zero indicates that the distribution is more balanced. If the value of a bias metric in the job analysis results file (analysis.json) is worse than its corresponding value in the baseline constraints file, a violation is logged. As an example, if the baseline constraint for the DPPL bias metric is 0.2, and the analysis result is 0.1, no violation is logged because 0.1 is closer to 0 than 0.2. However, if the analysis result is -0.3, a violation is logged because it is farther from 0 than the baseline constraint of 0.2.

{
    "version": "1.0",
    "violations": [{
        "facet": "Age",
        "facet_value": "40",
        "metric_name": "CI",
        "constraint_check_type": "bias_drift_check",
        "description": "Value 0.0751544567666083 does not meet the constraint requirement"
    }, {
        "facet": "Age",
        "facet_value": "40",
        "metric_name": "DPPL",
        "constraint_check_type": "bias_drift_check",
        "description": "Value -0.0791244970125596 does not meet the constraint requirement"
    }]
}