Insights ¦ A Pilot Study into Bias in Natural Language Processing

Published by: Financial Conduct Authority
Search for original: Link

Key Take Aways

Natural Language Processing (NLP) is rapidly evolving and holds significant promise for financial services, including customer support, automated advice, and document analysis, with AI potentially adding $13 trillion to global economic output by 2030.
Despite technological advances, NLP models such as word embeddings may perpetuate harmful social biases, which could lead to unfair or discriminatory outcomes in consumer-facing applications.
No single technique is sufficient for measuring bias in word embeddings; employing multiple metrics (e.g., WEAT and Direct Bias) provides a more comprehensive understanding of embedded stereotypes.
Existing bias mitigation methods, notably Hard Debiasing, often do not fully eliminate bias; in some cases, they worsen bias or reduce overall model quality.
Bias in embeddings can persist even after debiasing, evidenced by the ability of classifiers to predict prior b...