Bias Mitigation Agent: Optimizing Source Selection for Fair and Balanced Knowledge Retrieval

TL;DR

This paper introduces a Bias Mitigation Agent that uses a multi-agent system to optimize source selection, significantly reducing bias in knowledge retrieval for fairer and more balanced AI outputs.

Contribution

The paper presents a novel multi-agent system that orchestrates bias mitigation by optimizing source selection during knowledge retrieval in AI systems.

Findings

81.82% reduction in bias compared to baseline

Improved fairness and balance in retrieved content

Enhanced trustworthiness of AI-generated information

Abstract

Large Language Models (LLMs) have transformed the field of artificial intelligence by unlocking the era of generative applications. Built on top of generative AI capabilities, Agentic AI represents a major shift toward autonomous, goal-driven systems that can reason, retrieve, and act. However, they also inherit the bias present in both internal and external information sources. This significantly affects the fairness and balance of retrieved information, and hence reduces user trust. To address this critical challenge, we introduce a novel Bias Mitigation Agent, a multi-agent system designed to orchestrate the workflow of bias mitigation through specialized agents that optimize the selection of sources to ensure that the retrieved content is both highly relevant and minimally biased to promote fair and balanced knowledge dissemination. The experimental results demonstrate an 81.82\% reduction in bias compared to a baseline naive retrieval strategy.