  This study delves into the pervasive issue of gender issues in artificial
intelligence (AI), specifically within automatic scoring systems for
student-written responses. The primary objective is to investigate the presence
of gender biases, disparities, and fairness in generally targeted training
samples with mixed-gender datasets in AI scoring outcomes. Utilizing a
fine-tuned version of BERT and GPT-3.5, this research analyzes more than 1000
human-graded student responses from male and female participants across six
assessment items. The study employs three distinct techniques for bias
analysis: Scoring accuracy difference to evaluate bias, mean score gaps by
gender (MSG) to evaluate disparity, and Equalized Odds (EO) to evaluate
fairness. The results indicate that scoring accuracy for mixed-trained models
shows an insignificant difference from either male- or female-trained models,
suggesting no significant scoring bias. Consistently with both BERT and
GPT-3.5, we found that mixed-trained models generated fewer MSG and
non-disparate predictions compared to humans. In contrast, compared to humans,
gender-specifically trained models yielded larger MSG, indicating that
unbalanced training data may create algorithmic models to enlarge gender
disparities. The EO analysis suggests that mixed-trained models generated more
fairness outcomes compared with gender-specifically trained models.
Collectively, the findings suggest that gender-unbalanced data do not
necessarily generate scoring bias but can enlarge gender disparities and reduce
scoring fairness.
