Bias Detection and Mitigation in Synthetic Data

Abstract

Synthetic data generation can inadvertently perpetuate or amplify biases present in training data. This paper explores methods for detecting and mitigating bias in synthetic data generation to ensure fair and equitable AI systems.

Introduction

Bias in AI systems is a critical concern that can lead to unfair outcomes and perpetuate social inequalities. Synthetic data generation, while offering privacy benefits, can introduce new challenges related to bias detection and mitigation.

Bias Detection Methods

We examine several approaches for bias detection:

• Statistical parity measures
• Equalized odds assessment
• Demographic parity analysis
• Fairness metrics evaluation

Mitigation Strategies

Our proposed mitigation strategies include:

• Pre-processing techniques
• In-processing modifications
• Post-processing adjustments
• Adversarial training approaches

Experimental Results

We evaluate our methods on multiple datasets and demonstrate significant improvements in fairness metrics while maintaining data utility.

Conclusion

Bias detection and mitigation in synthetic data is essential for responsible AI development. Our methods provide practical tools for ensuring fairness in synthetic data generation.