Reproduce “Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societies” on OpenResearch

About this paper

Vithya Yogarajan, Gillian Dobbie, Te Taka Keegan, Rostam J. Neuwirth

This paper provides a comprehensive overview of bias in pre-trained language models, arguing that existing detection and mitigation approaches often overlook under-represented societies and indigenous populations. It highlights how current methods, metrics, and datasets are predominantly Western-centric and proposes a framework for developing culturally sensitive and context-aware solutions to ensure more inclusive and equitable AI.

Key insights

• Bias enters pre-trained language models through intrinsic (training data) and extrinsic (fine-tuning/deployment) pathways, perpetuating societal inequalities.
• Current bias detection metrics and benchmark datasets primarily reflect Western cultural contexts and binary social categorizations, limiting their applicability to diverse global populations.
• Effective bias mitigation for under-represented societies requires culturally sensitive metrics, representative datasets, and collaborative, interdisciplinary approaches, moving beyond 'one-size-fits-all' solutions.

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