Censorship of Online Encyclopedias: Implications for NLP Models

Abstract

While artificial intelligence provides the backbone for many tools people use around the world, recent work has brought to attention that the algorithms powering AI are not free of politics, stereotypes, and bias. While most work in this area has focused on the ways in which AI can exacerbate existing inequalities and discrimination, very little work has studied how governments actively shape training data. We describe how censorship has affected the development of Wikipedia corpuses, text data which are regularly used for pre-trained inputs into NLP algorithms. We show that word embeddings trained on Baidu Baike, an online Chinese encyclopedia, have very different associations between adjectives and a range of concepts about democracy, freedom, collective action, equality, and people and historical events in China than its regularly blocked but uncensored counterpart - Chinese language Wikipedia. We examine the implications of these discrepancies by studying their use in downstream AI applications. Our paper shows how government repression, censorship, and self-censorship may impact training data and the applications that draw from them.

arXiv commentAccepted for publication at ACM FAccT 2021

References in corpus (8)

• Distributed Representations of Words and Phrases and their Compositionality
• Semantics derived automatically from language corpora contain human-like biases
• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
• Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes
• ERNIE: Enhanced Representation through Knowledge Integration
• Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them
• No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World
• NEZHA: Neural Contextualized Representation for Chinese Language Understanding

Cited by in corpus (2)

• Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools
• Mitigating harm in language models with conditional-likelihood filtration