Insights: FCA – A Pilot Study into Bias in Natural Language Processing

Summary
This research note, authored by the Financial Conduct Authority (FCA), investigates bias in word embeddings, a fundamental technology in natural language processing (NLP). The study explores how biases related to gender, ethnicity, age, and other demographic factors are encoded and how effectively these biases can be mitigated through techniques like Hard Debiasing. The document is part of FCA's efforts to foster safe AI use in financial services, aligned with regulatory frameworks like the Consumer Duty.
Key Take Aways

Bias in NLP: Word embeddings capture biases present in the text used for training, potentially causing harmful stereotypes in AI-driven applications.
Use Cases in Financial Services: Bias could affect customer-facing AI tools, including chatbots and financial advice systems.
Bias Measurement: Techniques like the Word Embedding Association Test (WEAT) and Direct Bias analysis were used to detect stereotypes.
Demographic Factors: The study assessed biases related...