Towards Identifying Social Bias in Dialog Systems: Framework, Dataset, and Benchmark

By Jingyan Zhou, Deng Jiawen, Fei Mi, Yitong Li, Yasheng Wang, Minlie Huang, Xin Jiang, Qun Liu, Helen Mei-Ling Meng. 2022

55 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Minlie Huang
2. Fei Mi
3. Hao Sun
4. Yasheng Wang
5. Zhexin Zhang
6. Jiawen Deng
7. Jiale Cheng
8. Lifeng Shang

In Natural Language Processing

1. Minlie Huang
2. Zhexin Zhang
3. Hao Sun
4. Jiawen Deng
5. Jiale Cheng
6. Yasheng Wang
7. Fei Mi
8. Lifeng Shang

In Safety, Robustness & Interpretability

1. Zhexin Zhang
2. Hao Sun
3. Minlie Huang
4. Jiawen Deng
5. Jiale Cheng
6. Yasheng Wang
7. Fei Mi

Citations in the corpus

• Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generation (2022, background): “, 2021), extracted real-world data (Gehman et al., 2020; Sheng et al., 2021; Schick et al., 2021; Deng et al., 2022; Zhou et al., 2022), crowdsourcing workers (Xu et al.”