AI Gender Bias, Disparities, and Fairness: Does Training Data Matter?
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866916585966403584 |
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| author | Latif, Ehsan Zhai, Xiaoming Liu, Lei |
| author_facet | Latif, Ehsan Zhai, Xiaoming Liu, Lei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_10833 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | AI Gender Bias, Disparities, and Fairness: Does Training Data Matter? Latif, Ehsan Zhai, Xiaoming Liu, Lei Computers and Society 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. |
| title | AI Gender Bias, Disparities, and Fairness: Does Training Data Matter? |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2312.10833 |