Machine learning models are increasingly used in critical decision-making
applications. However, these models are susceptible to replicating or even
amplifying bias present in real-world data. While there are various bias
mitigation methods and base estimators in the literature, selecting the optimal
model for a specific application remains challenging.
This paper focuses on binary classification and proposes FairGridSearch, a
novel framework for comparing fairness-enhancing models. FairGridSearch enables
experimentation with different model parameter combinations and recommends the
best one. The study applies FairGridSearch to three popular datasets (Adult,
COMPAS, and German Credit) and analyzes the impacts of metric selection, base
estimator choice, and classification threshold on model fairness.
The results highlight the significance of selecting appropriate accuracy and
fairness metrics for model evaluation. Additionally, different base estimators
and classification threshold values affect the effectiveness of bias mitigation
methods and fairness stability respectively, but the effects are not consistent
across all datasets. Based on these findings, future research on fairness in
machine learning should consider a broader range of factors when building fair
models, going beyond bias mitigation methods alone.
Machine learning models are now being used in various applications that have significant impacts on our lives. However, these models are not perfect and can perpetuate biases present in the data they are trained on. This issue poses a challenge in selecting the most suitable model for a specific application.
This paper introduces FairGridSearch, a novel framework that addresses this challenge by allowing for experimentation with different model parameter combinations and recommending the best one. FairGridSearch focuses on binary classification tasks and aims to enhance fairness in the models.
The study conducted experiments using three popular datasets: Adult, COMPAS, and German Credit. The goal was to analyze the effects of different factors, such as metric selection, base estimator choice, and classification threshold, on the fairness of the models.
The research findings emphasize the importance of selecting appropriate accuracy and fairness metrics for evaluating these models. It is crucial to consider not only how well the model performs in terms of accuracy but also how it addresses fairness concerns. By doing so, decision-makers can gain a better understanding of the trade-offs between accuracy and fairness in these models.
Furthermore, the study highlights that the choice of base estimator and classification threshold can significantly impact the effectiveness of bias mitigation methods and fairness stability. However, it is essential to note that these effects are not consistent across all datasets. This suggests that different datasets may require tailored approaches to ensure fairness in models.
From an interdisciplinary perspective, this research underscores the complexity of fairness in machine learning. Building fair models goes beyond just implementing bias mitigation methods. It requires considering a broader range of factors such as data selection, model selection, and evaluation metrics. Researchers and practitioners working in machine learning, data science, and ethics should collaborate to explore potential solutions and develop guidelines to ensure fair and reliable decision-making models.
In conclusion, this paper sheds light on the challenges and considerations associated with fairness in machine learning models. FairGridSearch offers a valuable framework for comparing fairness-enhancing models and highlights the need for multi-disciplinary research approaches to address fairness concerns in machine learning.