首页|University of Alberta Researcher Broadens Understanding of Machine Learning (A C omparison of Bias Mitigation Techniques for Educational Classification Tasks Usi ng Supervised Machine Learning)
University of Alberta Researcher Broadens Understanding of Machine Learning (A C omparison of Bias Mitigation Techniques for Educational Classification Tasks Usi ng Supervised Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news originating from Edmonton, Cana da, by NewsRx editors, the research stated, "Machine learning (ML) has become in tegral in educational decision-making through technologies such as learning anal ytics and educational data mining. However, the adoption of machine learning-dri ven tools without scrutiny risks perpetuating biases." The news reporters obtained a quote from the research from University of Alberta : "Despite ongoing efforts to tackle fairness issues, their application to educa tional datasets remains limited. To address the mentioned gap in the literature, this research evaluates the effectiveness of four bias mitigation techniques in an educational dataset aiming at predicting students' dropout rate. The overarc hing research question is: "How effective are the techniques of reweighting, res ampling, and Reject Option-based Classification (ROC) pivoting in mitigating the predictive bias associated with high school dropout rates in the HSLS:09 datase t?' The effectiveness of these techniques was assessed based on performance metr ics including false positive rate (FPR), accuracy, and F1 score. The study focus ed on the biological sex of students as the protected attribute. The reweighting technique was found to be ineffective, showing results identical to the baselin e condition. Both uniform and preferential resampling techniques significantly r educed predictive bias, especially in the FPR metric but at the cost of reduced accuracy and F1 scores."
University of AlbertaEdmontonCanadaNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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