International Journal of Science, Engineering and TechnologyAuthors: Devansh Shukla, Dr Kaneez Zainab
Abstract: Machine Learning (ML) has emerged as a fundamental technology driving intelligent decision-making across numerous sectors, including healthcare, finance, education, e-commerce, transportation, and public administration. The increasing reliance on machine learning systems has improved efficiency, automation, and predictive capabilities. However, concerns regarding bias in machine learning models have gained significant attention in recent years. Bias can arise from various stages of the machine learning lifecycle, including data collection, preprocessing, feature engineering, model development, and deployment. Such biases may lead to unfair outcomes, reinforce existing social inequalities, and adversely affect individuals belonging to underrepresented groups. The consequences of biased machine learning systems extend beyond technical inaccuracies and can influence employment opportunities, credit approval decisions, medical diagnoses, and criminal justice outcomes. This study examines the major sources of bias in machine learning models, evaluates their social and operational impacts, and proposes a Bias-Aware Machine Learning Framework (BA-MLF) for identifying, measuring, and mitigating algorithmic bias. The framework integrates fairness assessment, bias detection mechanisms, fairness-aware learning techniques, and continuous monitoring strategies. The proposed approach aims to improve transparency, accountability, and fairness while maintaining model performance. The findings highlight the importance of responsible artificial intelligence development and provide practical recommendations for reducing bias in real-world machine learning applications.
