Ensuring Ethical, Transparent, and Auditable Use of Education Data and Algorithms on AutoML
Authors: 
Kylie Griep, Leah Stone, Stephen Edwards, Malik Hill, Jialin Cui, Chengyuan Liu, Qinjin Jia, Yang SongAuthors Info & Claims
Kylie Griep, Leah Stone, Stephen Edwards, Malik Hill, Jialin Cui, Chengyuan Liu, Qinjin Jia, Yang SongAuthors Info & ClaimsPages 66 - 72
Abstract
Automated machine learning (AutoML) creates additional opportunities for less advanced users to build and test their own data mining models. Even though AutoML creates the models for the user, there is still technical knowledge and tools needed to evaluate those models, and due to the black-box nature of the machine learning models, problems can arise with regard to algorithmic biases and fairness. Such biases can escalate in future applications, necessitating a structured approach for fairness evaluation in AutoML. This involves defining fairness criteria, selecting appropriate metrics, assessing fairness across groups, and addressing biases. In the realm of educational data mining, where AutoML is prevalent, biases related to attributes like gender or race can lead to unethical outcomes. Since fairness metrics vary in definition and strength, and some may even contradict others, making fairness evaluation more complex. In this paper, ten fairness metrics were chosen, explored, and implemented on four AutoML tools, Vertex AI, AutoSklearn, AutoKeras, and PyCaret. We identified two open educational datasets and built both prediction and classification models on those AutoML frameworks. We report our work in evaluating different machine learning models created by AutoML and provide discussions about the challenges in evaluating fairness in those models and our effort to mitigate and resolve the problems of algorithmic bias in educational data mining.
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December 2023
252 pages
ISBN:9798400709241
DOI:10.1145/3639479
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Published: 28 February 2024
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MLNLP 2023
MLNLP 2023: 2023 6th International Conference on Machine Learning and Natural Language Processing
December 27 - 29, 2023
Sanya, China
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