Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification
Project Overview
This document investigates the application of Large Language Models (LLMs) in education, emphasizing their role in promoting fairness in classification tasks. Specifically, it highlights the increasing attention given to advanced LLMs such as GPT-4, LLaMA-2, and Gemini, particularly in their capacity to address and reduce biases related to gender and race. The study presents a novel framework for embedding fairness definitions directly into LLM prompts, assessing the models' understanding and application of these fairness criteria. The findings illustrate the potential of in-context learning, revealing that LLMs can generate equitable outcomes in various classification scenarios. Overall, this research underscores the significant impact of generative AI on educational practices by enhancing fairness and reducing bias, thereby contributing to more equitable educational environments.
Key Applications
Using LLMs to classify income based on demographic data
Context: Educational institutions and policy makers assessing fairness in income classification tasks, particularly relevant for programs addressing economic disparities.
Implementation: LLMs were prompted to classify income based on demographic factors using in-context learning with fairness definitions.
Outcomes: Improvement in accuracy and fairness metrics, particularly with GPT-4, showcasing the potential of LLMs to understand fairness in classification tasks.
Challenges: LLMs may still exhibit biases in their predictions, particularly towards certain demographic groups, indicating that they are not entirely free from bias.
Implementation Barriers
Technical and Practical Barrier
Existing biases in training data can lead to biased predictions by LLMs, affecting their fairness outcomes. Not all users have the expertise or resources to fine-tune LLMs effectively for fairness.
Proposed Solutions: Incorporate fairness definitions into prompts and use in-context learning techniques to guide LLMs towards fairer outputs. Additionally, develop user-friendly frameworks that allow non-experts to implement fairness guidelines in LLM applications.
Project Team
Garima Chhikara
Researcher
Anurag Sharma
Researcher
Kripabandhu Ghosh
Researcher
Abhijnan Chakraborty
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Garima Chhikara, Anurag Sharma, Kripabandhu Ghosh, Abhijnan Chakraborty
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai