Understanding and Mitigating Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks

📰 ArXiv cs.AI

Learn to identify and mitigate bias inheritance in LLM-based data augmentation to improve fairness and robustness in downstream tasks

advanced Published 6 May 2026

Action Steps

Identify potential biases in LLM training data using techniques like data auditing and bias detection tools
Analyze the impact of bias inheritance on downstream tasks using metrics like fairness and robustness
Apply debiasing techniques to LLMs like data preprocessing and regularization to mitigate bias inheritance
Evaluate the effectiveness of debiasing techniques using metrics like accuracy and fairness
Implement bias-aware data augmentation methods to generate synthetic data that is fair and representative

Who Needs to Know This

Data scientists and AI engineers working with LLMs and data augmentation techniques can benefit from understanding bias inheritance to develop more fair and robust models

Key Insight

💡 Bias inheritance occurs when LLMs propagate and amplify inherent biases in their training data, affecting fairness and robustness in downstream tasks

Key Takeaways

Learn to identify and mitigate bias inheritance in LLM-based data augmentation to improve fairness and robustness in downstream tasks

Full Article

Title: Understanding and Mitigating Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks

Abstract:
arXiv:2502.04419v3 Announce Type: replace-cross Abstract: Generating synthetic datasets via large language models (LLMs) has emerged as a promising approach to improve LLM performance. However, LLMs inherently reflect biases in their training data, leading to a critical challenge: when models are trained on synthetic data, they may propagate and amplify the inherent biases that can significantly impact fairness and robustness on downstream tasks-a phenomenon we term bias inheritance. This work p

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