Bias Mitigation Agent: Optimizing Source Selection for Fair and Balanced Knowledge Retrieval
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914006032187392 |
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| author | Singh, Karanbir Muppiri, Deepak Ngu, William |
| author_facet | Singh, Karanbir Muppiri, Deepak Ngu, William |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18724 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bias Mitigation Agent: Optimizing Source Selection for Fair and Balanced Knowledge Retrieval Singh, Karanbir Muppiri, Deepak Ngu, William Artificial Intelligence Computation and Language 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. |
| title | Bias Mitigation Agent: Optimizing Source Selection for Fair and Balanced Knowledge Retrieval |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2508.18724 |