BibTeX

@inProceedings{munozsanchez-etal-2024-name-339981,
	title        = {Name Biases in Automated Essay Assessment},
	abstract     = {Artificial intelligence is being deployed in high-stakes situations, such as automated grading of second language essays in proficiency assessment. While they can improve the opportunities students have (education, work opportunities, etc.), such systems often display human-like biases. Aldrin (2017) notes that human graders have a slight bias based on names appearing in essay texts. We aim to identify whether the same pattern holds in automated systems. 
In this study we aim to answer the following research questions: 
1) Does changing given names inside a second language learner essay affect the way the text is graded? 
2) How much does this differ between feature-based machine learning and deep learning? 

For this, we use a de-anonymized (i.e. original) version of the Swell-pilot corpus of second language Swedish learner essays (Volodina 2016), which consists of 502 essays annotated with CEFR levels as our source data. 
First, we compile four lists of given names inspired by those of Aldrin (2017): traditional Swedish names; modern Swedish names of Anglo-American origin; Finnish names (due to the close sociocultural links between both countries); and names of Arabic origin (the most prominent group of learners in the corpus). 
Second, we create a diagnostic dataset to identify biases in the classification task. We select SweLL-pilot essays in which a given name appears only once. Then, we generate an essay version for each name on the lists by substituting the name in the original text with one from the list. 
Third, we fine-tune a BERT (Devlin et al. 2019) model on the original SweLL-pilot data to predict the CEFR level of a given essay and compare it to an existing feature-based model (Pilan 2016). 

Finally, we test the two models and compare the equality of opportunity between the different given name groups on the diagnostic dataset. },
	booktitle    = {The 28th International Congress of Onomastic Sciences (ICOS 28)},
	author       = {Muñoz Sánchez, Ricardo and Dobnik, Simon and Lindström Tiedemann, Therese and Szawerna, Maria Irena and Volodina, Elena},
	year         = {2024},
}