BIASINSPECTOR: Detecting Bias in Structured Data through LLM Agents

• URL: http://arxiv.org/abs/2504.04855v1
• Date: Mon, 07 Apr 2025 09:12:00 GMT
• Title: BIASINSPECTOR: Detecting Bias in Structured Data through LLM Agents
• Authors: Haoxuan Li, Mingyu Derek Ma, Jen-tse Huang, Zhaotian Weng, Wei Wang, Jieyu Zhao,
• Abstract summary: We introduce the first end-to-end, multi-agent synergy framework, BIASINSPECTOR, for automatic bias detection in structured data.<n>It first develops a multi-stage plan to analyze user-specified bias detection tasks and then implements it with a diverse and well-suited set of tools.<n>It achieves exceptional overall performance in structured data bias detection, setting a new milestone for fairer data applications.
• Score: 27.159150467166732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting biases in structured data is a complex and time-consuming task. Existing automated techniques are limited in diversity of data types and heavily reliant on human case-by-case handling, resulting in a lack of generalizability. Currently, large language model (LLM)-based agents have made significant progress in data science, but their ability to detect data biases is still insufficiently explored. To address this gap, we introduce the first end-to-end, multi-agent synergy framework, BIASINSPECTOR, designed for automatic bias detection in structured data based on specific user requirements. It first develops a multi-stage plan to analyze user-specified bias detection tasks and then implements it with a diverse and well-suited set of tools. It delivers detailed results that include explanations and visualizations. To address the lack of a standardized framework for evaluating the capability of LLM agents to detect biases in data, we further propose a comprehensive benchmark that includes multiple evaluation metrics and a large set of test cases. Extensive experiments demonstrate that our framework achieves exceptional overall performance in structured data bias detection, setting a new milestone for fairer data applications.

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