Your AI Model Is Biased. Your Real Data Is Hiding It. Synthetic Databases Can Find It First.

The provided text outlines an important process for detecting and mitigating bias in machine learning models, especially those used in decision-making processes that affect people. The key points are:

1. Bias Detection with Synthetic Data: Using synthetic data to create a balanced representation of different segments (e.g., rural vs. suburban applicants) helps detect biases that might be hidden when using real-world datasets where certain groups may be underrepresented.

2. Steps for Bias Detection:

   • Train the model on historical data.
   • Create a synthetic dataset with controlled segment proportions to ensure balanced representation of different segments.
   • Validate the model's performance and fairness metrics (like AUC, Disparate Impact ratio) using this synthetic dataset.
   • Retrain the model if any segment fails the fairness audit.

3. Why Synthetic Data is Necessary:

   • Real-world datasets often have imbalanced representations of various groups, leading to underpowered audits for smaller segments.
   • Synthetic data allows you to control and balance these proportions, making it easier to detect biases that might be hidden in real-world data due to small sample sizes.

4. Bias Detection Checklist:

   • Compute AUC separately for every protected or at-risk group.
   • Dis

Read the full article at Towards AI - Medium

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