Sentiment Bias in Predictive Text Recommendations Results in Biased Writing

Kenneth C. Arnold, K. Chauncey, Krzysztof Z Gajos
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引用次数: 27

Abstract

Prior research has demonstrated that intelligent systems make biased decisions because they are trained on biased data. As people increasingly leverage intelligent systems to enhance their productivity and creativity, could system biases affect what people create? We demonstrate that in at least one domain (writing restaurant reviews), biased system behavior leads to biased human behavior: People presented with phrasal text entry shortcuts that were skewed positive wrote more positive reviews than they did when presented with negative-skewed shortcuts. This result contributes to the pertinent debate about the role of intelligent systems in our society.
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预测性文本推荐中的情感偏差导致有偏见的写作
先前的研究表明,智能系统之所以会做出有偏见的决定,是因为它们接受的是有偏见的数据训练。随着人们越来越多地利用智能系统来提高生产力和创造力,系统偏见会影响人们的创造吗?我们证明了至少在一个领域(写餐馆评论),有偏见的系统行为会导致有偏见的人类行为:人们在看到有积极倾向的短语文本输入快捷方式时,会比看到有消极倾向的快捷方式时写出更多积极的评论。这一结果有助于有关智能系统在我们社会中的作用的相关辩论。
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