论文信息 - Exploring Social Bias in Chatbots using Stereotype Knowledge

Exploring Social Bias in Chatbots using Stereotype Knowledge

Exploring social bias in chatbot is an important, yet relatively unexplored problem. In this paper, we propose an approach to understand social bias in chatbots by leveraging stereotype knowledge. It allows interesting comparison of bias between chatbots and humans, and provides intuitive analysis of existing chatbots by borrowing the finer-grain concepts of sexism and racism.

[1] J. B. Mcconahay,et al. Modern racism, ambivalence, and the Modern Racism Scale. , 1986 .

[2] Adam Tauman Kalai,et al. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings , 2016, NIPS.

[3] Jieyu Zhao,et al. Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods , 2018, NAACL.

[4] Lucy Vasserman,et al. Measuring and Mitigating Unintended Bias in Text Classification , 2018, AIES.

[5] Saif Mohammad,et al. Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems , 2018, *SEMEVAL.

[6] Dirk Hovy,et al. Learning a POS tagger for AAVE-like language , 2016, NAACL.

[7] Jieyu Zhao,et al. Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints , 2017, EMNLP.

[8] S. Lemon,et al. The Ambivalent Sexism Inventory : Differentiating Hostile and Benevolent Sexism , 2001 .

[9] Luke S. Zettlemoyer,et al. AllenNLP: A Deep Semantic Natural Language Processing Platform , 2018, ArXiv.

[10] Pascale Fung,et al. Reducing Gender Bias in Abusive Language Detection , 2018, EMNLP.

[11] David García,et al. It's a Man's Wikipedia? Assessing Gender Inequality in an Online Encyclopedia , 2015, ICWSM.