Beyond Accuracy: Measuring Bias Acknowledgment in Chain-of-Thought Reasoning for Responsible AI Evaluation

Xian Sun, Yingshuo Wang, Wei Gao, Lingdong Kong, Zexin Zhuang, Zhichao Fan, Wenlong Dong, Hrishikesh Paranjape, Zhiyuan Zheng

Keywords: Chain-of-Thought, Bias Robustness, Responsible AI, Language Models

TL;DR: We introduce a trace-level diagnostic showing that reasoning models can have similar answer-level robustness under bias while differing sharply in whether their chain-of-thought explicitly acknowledges the biasing content.

Abstract: Reasoning models are increasingly used in settings where the final answer is not the only object of review: educational tools may show students intermediate steps, decision-support systems may require human oversight, and audit workflows may inspect traces for misleading or biased input. In such settings, two responses can receive the same final-answer score while differing in whether the trace explicitly flags injected biasing content. Accuracy-only evaluation collapses these cases. We study this gap as a measurement blind spot for responsible evaluation and introduce a minimal trace-level diagnostic with two axes: suseptibility (whether the bias breaks a previously correct answer) and acknowledgment (whether the trace contains a rubric-defined surface reference to the injected content). Across thousands of biased GSM8K trials, GPT-4o and Claude Sonnet 4 have similar susceptibility rates (1.3% vs. 1.2%) but substantially different acknowledgment rates (13.0% vs. 75.0%) under the same rubric.

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Submission Number: 338