  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.
