Ablation Study of a Fairness Auditing Agentic System for Bias Mitigation in Early-Onset Colorectal Cancer Detection
This work addresses fairness auditing in clinical AI for early-onset colorectal cancer, which has documented demographic disparities, but it is incremental as it builds on existing agentic and retrieval-augmented methods.
This study tackled the problem of algorithmic bias in AI for early-onset colorectal cancer detection by evaluating an agentic AI system for fairness auditing, finding that an agent with retrieval-augmented generation achieved the highest semantic similarity to expert references across different model sizes.
Artificial intelligence (AI) is increasingly used in clinical settings, yet limited oversight and domain expertise can allow algorithmic bias and safety risks to persist. This study evaluates whether an agentic AI system can support auditing biomedical machine learning models for fairness in early-onset colorectal cancer (EO-CRC), a condition with documented demographic disparities. We implemented a two-agent architecture consisting of a Domain Expert Agent that synthesizes literature on EO-CRC disparities and a Fairness Consultant Agent that recommends sensitive attributes and fairness metrics for model evaluation. An ablation study compared three Ollama large language models (8B, 20B, and 120B parameters) across three configurations: pretrained LLM-only, Agent without Retrieval-Augmented Generation (RAG), and Agent with RAG. Across models, the Agent with RAG achieved the highest semantic similarity to expert-derived reference statements, particularly for disparity identification, suggesting agentic systems with retrieval may help scale fairness auditing in clinical AI.