AI Gender Bias, Disparities, and Fairness: Does Training Data Matter?

• URL: http://arxiv.org/abs/2312.10833v2
• Date: Tue, 26 Dec 2023 00:30:38 GMT
• Title: AI Gender Bias, Disparities, and Fairness: Does Training Data Matter?
• Authors: Ehsan Latif, Xiaoming Zhai, and Lei Liu
• Abstract summary: This study delves into the pervasive issue of gender issues in artificial intelligence (AI) It analyzes more than 1000 human-graded student responses from male and female participants across six assessment items. Results indicate that scoring accuracy for mixed-trained models shows an insignificant difference from either male- or female-trained models.
• Score: 3.509963616428399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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