Zhiltsova, AlinaCaton, SimonMulwa, Catherine2023-07-242023-07-242019-121613-0073http://hdl.handle.net/20.500.13012/162Proceedings for the 27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science NUI Galway, Galway, Ireland, December 5-6th, 2019. Currently the demand for text analytics grows due to the fact that textual data is being created online in large amounts. A number of tools are available for various tasks related to natural language process- ing such as sentiment analysis and text pre-processing. The majority of these tools are trained using unrepresentative data, which may lead to unintended biases altering the results. Previous research indicates that sentiment analysis tools show gender and race biases, and word embed- dings discriminate against women. This research investigates previously undefined non-native speaker bias in sentiment analysis, i.e. unintended discrimination against English texts written by non-native speakers of English. Non-native speakers of English tend to use cognates, English words that have origin in the speaker’s language. To measure the non- native speaker bias in 4 lexicon-based sentiment analysis systems, a new Cognate Equity Evaluation Corpus was created, based on previous work in the literature for measuring racial and gender biases. The tools gave significantly different scores to English texts with features of non-native speakers. The bias discovered in lexicon-based tools was mitigated by updating 4 lexicons for English cognates in 3 languages. This paper pro- poses a generalisable framework for measuring and mitigating non-native speaker bias.enFairness in machine learningNatural language processingBias mitigationNon-native speaker biasSentiment analysisArticle