Detecting and Quantifying Bias in Machine Learning Models

Machine learning (ML) systems are increasingly being used to support decisions in areas such as healthcare, finance, education, hiring, and criminal justice. While these technologies can improve efficiency and accuracy, they may also produce unfair outcomes when biases present in data are learned and reproduced by algorithms. Such biases can lead to discrimination against certain individuals or groups, undermining trust in AI-driven systems. This study examines how bias can be detected and measured in machine learning models using established fairness metrics and evaluation techniques. A quantitative approach is adopted to assess model performance across different demographic groups. Metrics such as demographic parity, disparate impact, equal opportunity, and predictive parity are used to evaluate fairness. The findings indicate that machine learning models can exhibit significant disparities among demographic groups even when they achieve high predictive accuracy. The study emphasizes the importance of incorporating fairness assessments throughout the machine learning lifecycle and recommends the adoption of bias mitigation strategies to ensure more equitable outcomes. The research contributes to the development of responsible and trustworthy artificial intelligence systems.