Comprehending and Mitigating Feature Bias in Machine Learning Models for Ethical AI
Youssef Hani Abdelfattah Mohamed
Department of Computer Science, Minia University, Minya, Egypt
Keywords: Feature Bias, Machine Learning, Algorithmic Fairness, Ethical Artificial Intelligence, Data Diversity
Abstract
The critical importance of understanding and rectifying feature bias in machine learning (ML) models is pivotal in the development of fair and reliable artificial intelligence (AI) systems. This study delves into the nature and origins of feature bias, which arises when ML models base decisions on skewed or unrepresentative data, leading to potentially biased or erroneous outcomes for certain demographics. Key factors contributing to this bias include prejudices in data collection, historical and societal biases in the data, and biases inherent in the data labeling process. The ramifications of such biases are profound, potentially resulting in discriminatory practices and stereotyping, thereby diminishing the model's effectiveness in diverse real-world applications. The research presents methodologies for addressing feature bias, emphasizing the importance of diverse data sets and regular bias auditing using statistical methods to identify and quantify biases. In the realm of model development, the focus is on algorithmic fairness, including the implementation of fairness constraints or objectives during the model training process, and the careful selection and engineering of features to avoid proxies for sensitive attributes like race or gender. The paper also highlights the significance of diverse testing scenarios, independent review of model predictions, continuous monitoring through feedback loops, and regular model updates to reflect changing societal norms and values.
Author Biography
Youssef Hani Abdelfattah Mohamed, Department of Computer Science, Minia University, Minya, Egypt
