An Agentic Evaluation Architecture for Historical Bias Detection in Educational Textbooks

History textbooks often contain implicit biases, nationalist framing, and selective omissions that are difficult to audit at scale. We propose an agentic evaluation architecture comprising a multimodal screening agent, a heterogeneous jury of five evaluative agents, and a meta-agent for verdict synthesis and human escalation. A central contribution is a Source Attribution Protocol that distinguishes textbook narrative from quoted historical sources, preventing the misattribution that causes syst

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ethics.ai (9 April 2026), “An Agentic Evaluation Architecture for Historical Bias Detection in Educational Textbooks,” evidence record 6152, https://ethics.ai/record/6152 (originally published by arXiv).

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