Algorithmic Fairness

Pessach, Dana; Shmueli, Erez

doi:10.1007/978-3-031-24628-9_37

Cited by 17 publications

(5 citation statements)

References 37 publications

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“…Hence, it emerged as the optimal technique within the studied dataset of HSLS:09. This study is notable for its application of bias mitigation techniques to an educational dataset of HSLS:09, contributing to the topic beyond commonly studied datasets like COMPAS [9][10][11].…”

Section: Discussionmentioning

confidence: 99%

“…From a legal standpoint, fairness entails the absence of disparate treatment (direct discrimination) and the prevention of disparate impact resulting from seemingly neutral actions (indirect discrimination) to individuals based on their protected characteristics such as race or gender [11]. From a mathematical standpoint, fairness is characterized by the equality of the percentage of positive predictions across demographic subgroups, emphasizing demographic parity.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Finally, the notion of robustness for fairness in real-world applications is challenging due to the uniqueness of real-world data. Much research in algorithmic fairness relies on the same dataset, such as the COMPAS dataset [9,11]. However, each real-world dataset is unique, so algorithms that were proven robust on these datasets may become less effective in the actual implementation on other datasets.…”

Section: Dilemmasmentioning

confidence: 99%

“…Key methodological areas aimed at promoting fairness in ML include subgroup analysis, regularization, and calibration for classification tasks, as well as fair regression for regression tasks. Much of the research on fairness in ML utilizes the same datasets, such as the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) dataset or the German financial credit dataset [9][10][11]. However, the use of datasets in the educational context is limited.…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Bias Mitigation Techniques for Educational Classification Tasks Using Supervised Machine Learning

Wongvorachan,

Bulut,

Liu

et al. 2024

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Machine learning (ML) has become integral in educational decision-making through technologies such as learning analytics and educational data mining. However, the adoption of machine learning-driven tools without scrutiny risks perpetuating biases. Despite ongoing efforts to tackle fairness issues, their application to educational 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 overarching research question is: “How effective are the techniques of reweighting, resampling, and Reject Option-based Classification (ROC) pivoting in mitigating the predictive bias associated with high school dropout rates in the HSLS:09 dataset?" The effectiveness of these techniques was assessed based on performance metrics including false positive rate (FPR), accuracy, and F1 score. The study focused on the biological sex of students as the protected attribute. The reweighting technique was found to be ineffective, showing results identical to the baseline condition. Both uniform and preferential resampling techniques significantly reduced predictive bias, especially in the FPR metric but at the cost of reduced accuracy and F1 scores. The ROC pivot technique marginally reduced predictive bias while maintaining the original performance of the classifier, emerging as the optimal method for the HSLS:09 dataset. This research extends the understanding of bias mitigation in educational contexts, demonstrating practical applications of various techniques and providing insights for educators and policymakers. By focusing on an educational dataset, it contributes novel insights beyond the commonly studied datasets, highlighting the importance of context-specific approaches in bias mitigation.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Dilemmasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comparison of Bias Mitigation Techniques for Educational Classification Tasks Using Supervised Machine Learning

Wongvorachan,

Bulut,

Liu

et al. 2024

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“…For example, the concept of "equal opportunity" in GF refers to a fairness metric, defined as the difference of the true positive rates between two groups, and a fairness constraint, expressed as a difference of the true positive rates close to zero. 48 Often, the context in which the term fairness is used will clarify whether it refers to a metric or a constraint.…”

Section: Measuring Algorithmic Fairnessmentioning

confidence: 99%

Algorithmic Individual Fairness and Healthcare: A Scoping Review

Anderson,

Visweswaran

2024

Preprint

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Statistical and artificial intelligence algorithms are increasingly being developed for use in healthcare. These algorithms may reflect biases that magnify disparities in clinical care, and there is a growing need for understanding how algorithmic biases can be mitigated in pursuit of algorithmic fairness. Individual fairness in algorithms constrains algorithms to the notion that "similar individuals should be treated similarly." We conducted a scoping review on algorithmic individual fairness to understand the current state of research in the metrics and methods developed to achieve individual fairness and its applications in healthcare. We searched three databases, PubMed, ACM Digital Library, and IEEE Xplore, for algorithmic individual fairness metrics, algorithmic bias mitigation, and healthcare applications. Our search was restricted to articles published between January 2013 and September 2023. We identified 1,886 articles through database searches and manually identified one article from which we included 30 articles in the review. Data from the selected articles were extracted, and the findings were synthesized. Based on the 30 articles in the review, we identified several themes, including philosophical underpinnings of fairness, individual fairness metrics, mitigation methods for achieving individual fairness, implications of achieving individual fairness on group fairness and vice versa, fairness metrics that combined individual fairness and group fairness, software for measuring and optimizing individual fairness, and applications of individual fairness in healthcare. While there has been significant work on algorithmic individual fairness in recent years, the definition, use, and study of individual fairness remain in their infancy, especially in healthcare. Future research is needed to apply and evaluate individual fairness in healthcare comprehensively.

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