AI Safety Auditor

AI Safety Auditor

The AI Safety Auditor skill guides you through comprehensive evaluation of AI systems for safety, fairness, and responsible deployment. As AI systems become more capable and widespread, ensuring they behave safely and equitably is critical for both ethical reasons and business risk management.

This skill covers bias detection and mitigation, safety testing for harmful outputs, robustness evaluation, privacy considerations, and documentation for compliance. It helps you build AI systems that are not only effective but trustworthy and aligned with human values.

Whether you are deploying an LLM-powered product, building a classifier with real-world impact, or evaluating third-party AI services, this skill ensures you identify and address potential harms before they affect users.

Core Workflows

Workflow 1: Conduct Bias Audit

1. Define protected attributes:
   • Demographics: race, gender, age, disability
   • Other sensitive attributes relevant to context
2. Measure performance disparities:

   def bias_audit(model, test_data, protected_attribute):
       groups = test_data.groupby(protected_attribute)
       metrics = {}
   
       for group_name, group_data in groups:
           predictions = model.predict(group_data.features)
           metrics[group_name] = {
               "accuracy": accuracy_score(group_data.labels, predictions),
               "false_positive_rate": fpr(group_data.labels, predictions),
               "false_negative_rate": fnr(group_data.labels, predictions),
               "selection_rate": predictions.mean()
           }
   
       return {
           "group_metrics": metrics,
           "demographic_parity": max_disparity(metrics, "selection_rate"),
           "equalized_odds": max_disparity(metrics, ["fpr", "fnr"]),
           "predictive_parity": max_disparity(metrics, "accuracy")
       }
   

3. Identify significant disparities:
   • Statistical significance testing
   • Compare to acceptable thresholds
   • Understand root causes
4. Document findings
5. Plan mitigation if needed