We investigate the potential for nationality biases in natural language
processing (NLP) models using human evaluation methods. Biased NLP models can
perpetuate stereotypes and lead to algorithmic discrimination, posing a
significant challenge to the fairness and justice of AI systems. Our study
employs a two-step mixed-methods approach that includes both quantitative and
qualitative analysis to identify and understand the impact of nationality bias
in a text generation model. Through our human-centered quantitative analysis,
we measure the extent of nationality bias in articles generated by AI sources.
We then conduct open-ended interviews with participants, performing qualitative
coding and thematic analysis to understand the implications of these biases on
human readers. Our findings reveal that biased NLP models tend to replicate and
amplify existing societal biases, which can translate to harm if used in a
sociotechnical setting. The qualitative analysis from our interviews offers
insights into the experience readers have when encountering such articles,
highlighting the potential to shift a reader's perception of a country. These
findings emphasize the critical role of public perception in shaping AI's
impact on society and the need to correct biases in AI systems.

研究了自然语言处理（NLP）模型中的国籍偏见及其对公众看法的影响，并发现具有偏见的 NLP 模型会复制和放大现有社会偏见，导致人工智能系统的不公平和不正义，强调了公众看法在塑造人工智能对社会的影响中的关键作用以及纠正人工智能系统中的偏见的必要性。