Debiasing large language models: research opportunities.

Area of Science:

• Artificial Intelligence
• Computer Science
• Societal Impact of Technology

Background:

• Large language models (LLMs) are increasingly used in critical sectors like healthcare and finance.
• LLMs can inherit and amplify societal biases from training data, algorithms, and user interactions, raising concerns for equality and fairness.
• Current research on LLM bias predominantly focuses on the US and Europe, neglecting other societal contexts.

Purpose of the Study:

• To experimentally evaluate existing bias metrics and debiasing techniques for large language models within the unique context of Aotearoa New Zealand.
• To identify research gaps and discuss current and future research opportunities for addressing LLM bias in New Zealand.

Main Methods:

• Experimental evaluation of established bias metrics.
• Assessment of existing debiasing techniques.
• Literature review to identify research gaps and current work.

Main Results:

• The study provides an experimental assessment of bias metrics and debiasing techniques tailored to the New Zealand context.
• Identified specific research gaps relevant to New Zealand's unique social, cultural, and historical landscape.
• Outlined current and ongoing research initiatives in the field.

Conclusions:

• There is a critical need to adapt and develop LLM bias research for non-Western, diverse societies like New Zealand.
• The findings offer a roadmap for the New Zealand research community to contribute to equitable AI development.
• Further research is essential to ensure large language models are fair and unbiased across diverse global populations.