Do Not Harm Protected Groups in Debiasing Language Representation Models
This work addresses fairness issues in AI for researchers and practitioners by highlighting incremental risks in debiasing methods that may inadvertently harm the groups they aim to protect.
The paper tackles the problem that debiasing techniques for Language Representation Models can degrade performance for all demographic groups, including protected ones, in downstream tasks, showing this negative effect through evaluations on a real-world text classification task.
Language Representation Models (LRMs) trained with real-world data may capture and exacerbate undesired bias and cause unfair treatment of people in various demographic groups. Several techniques have been investigated for applying interventions to LRMs to remove bias in benchmark evaluations on, for example, word embeddings. However, the negative side effects of debiasing interventions are usually not revealed in the downstream tasks. We propose xGAP-DEBIAS, a set of evaluations on assessing the fairness of debiasing. In this work, We examine four debiasing techniques on a real-world text classification task and show that reducing biasing is at the cost of degrading performance for all demographic groups, including those the debiasing techniques aim to protect. We advocate that a debiasing technique should have good downstream performance with the constraint of ensuring no harm to the protected group.