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Scientific modeling is a vital educational practice that helps students apply scientific knowledge to real-world phenomena. Despite advances in AI, challenges in accurately assessing such models persist, primarily due to the complexity of cognitive constructs and data imbalances in educational settings. This study addresses these challenges by employing diverse analytic strategies, including the Synthetic Minority Over-sampling Technique (SMOTE), aimed at enhancing fairness and efficacy in automated scoring systems. We analyze the impact of these strategies through a robust methodology, utilizing a combination of tenfold cross-validation and independent testing phases to ensure the reliability of AI assessments. Our findings highlight the effectiveness of deep learning AI in mirroring human judgment, with improvements in accuracy, precision, recall, and F1 scores across varied model assessments. Specifically, the application of SMOTE significantly improved the scoring fairness for minority class instances, which are often underrepresented in educational datasets. This study also delves into the discrepancies between AI and human evaluations, particularly in interpreting creatively expressed student models, which reveals the areas where AI technologies require further enhancements to better align with human evaluative standards. This study lays a foundation for future research to explore advanced AI techniques and training strategies, thus promoting fair and supportive feedback mechanisms that enhance student learning and creativity. By advancing AI applications in science education, this research addresses essential challenges in the automated analysis of complex student responses and supports broader academic goals.
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On Improving Fairness of AI Models with Synthetic Minority Oversampling Techniques
Biased AI models result in unfair decisions. In response, a number of algorithmic solutions have been engineered to mitigate bias, among which the Synthetic Minority Oversampling Technique (SMOTE) has been studied, to an extent. Although the SMOTE technique and its variants have great potentials to help improve fairness, there is little theoretical justification for its success. In addition, formal error and fairness bounds are not clearly given. This paper attempts to address both issues. We prove and demonstrate that synthetic data generated by oversampling underrepresented groups can mitigate algorithmic bias in AI models, while keeping the predictive errors bounded. We further compare this technique to the existing state-of-the-art fair AI techniques on five datasets using a variety of fairness metrics. We show that this approach can effectively improve fairness even when there is a significant amount of label and selection bias, regardless of the baseline AI algorithm.
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- Award ID(s):
- 1939728
- PAR ID:
- 10549626
- Publisher / Repository:
- Society for Industrial and Applied Mathematics
- Date Published:
- ISBN:
- 978-1-61197-765-3
- Page Range / eLocation ID:
- 874 - 882
- Subject(s) / Keyword(s):
- AI fairness sensitive feature synthetic data SMOTE
- Format(s):
- Medium: X
- Location:
- Minneapolis-St. Paul Twin Cities, MN, USA
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript
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- The DOI is not currently available.
