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Group bias in natural language processing tasks manifests as disparities insystem error rates across texts authorized by different demographic groups,typically disadvantaging minority groups. Dataset balancing has been shown tobe effective at mitigating bias, however existing approaches do not directlyaccount for correlations between author demographics and linguistic variables,limiting their effectiveness. To achieve Equal Opportunity fairness, such asequal job opportunity without regard to demographics, this paper introduces asimple, but highly effective, objective for countering bias using balancedtraining. We extend the method in the form of a gated model, which incorporatesprotected attributes as input, and show that it is effective at reducing biasin predictions through demographic input perturbation, outperforming all otherbias mitigation techniques when combined with balanced training.

Balancing out Bias: Achieving Fairness Through Balanced Training