The research of open-domain dialog systems has been greatly prospered by
neural models trained on large-scale corpora, however, such corpora often
introduce various safety problems (e.g., offensive languages, biases, and toxic
behaviors) that significantly hinder the deployment of dialog systems in
practice. Among all these unsafe issues, addressing social bias is more complex
as its negative impact on marginalized populations is usually expressed
implicitly, thus requiring normative reasoning and rigorous analysis. In this
paper, we focus our investigation on social bias detection of dialog safety
problems. We first propose a novel Dial-Bias Frame for analyzing the social
bias in conversations pragmatically, which considers more comprehensive
bias-related analyses rather than simple dichotomy annotations. Based on the
proposed framework, we further introduce CDail-Bias Dataset that, to our
knowledge, is the first well-annotated Chinese social bias dialog dataset. In
addition, we establish several dialog bias detection benchmarks at different
label granularities and input types (utterance-level and context-level). We
show that the proposed in-depth analyses together with these benchmarks in our
Dial-Bias Frame are necessary and essential to bias detection tasks and can
benefit building safe dialog systems in practice.

本文提出了一种社会偏见检测的新框架 Dial-bias，通过该框架构建了中文社会偏见对话数据集，并建立了不同粒度和输入类型的对话偏见检测基准，旨在帮助实践中构建更安全的对话系统。