Bias-Aware Agent: Enhancing Fairness in AI-Driven Knowledge Retrieval

Karanbir Singh, William Ngu

Abstract:

Advancements in retrieving accessible information have evolved
faster in the last few years compared to the decades since the
internet's creation. Search engines, like Google, have been the #1
way to find relevant data. They have always relied on the user's
abilities to find the best information in its billions of links and
sources at everyone's fingertips. The advent of large language
models (LLMs) has completely transformed the field of
information retrieval. The LLMs excel not only at retrieving
relevant knowledge but also at summarizing it effectively, making
information more accessible and consumable for users. On top of
it, the rise of AI Agents has introduced another aspect to
information retrieval i.e. dynamic information retrieval which
enables the integration of real-time data such as weather forecasts,
and financial data with the knowledge base to curate context-aware
knowledge. However, despite these advancements the agents
remain susceptible to issues of bias and fairness –challenges deeply
rooted within the knowledge base and training of LLMs. This study
explores innovative approaches to bias-aware knowledge retrieval
by leveraging agentic framework and innovative use of bias
detectors as tools that identify and highlight inherent biases in
retrieved content. By empowering users with transparency and
awareness, this approach aims to foster more equitable information
systems and promote the development of responsible AI.