AI Ethics and Fairness in Machine Learning: Promoting Fairness in Critical Engineering Systems

The integration of AI and machine learning into engineering systems has revolutionized various domains, including autonomous systems, renewable energy management, and industrial automation. While these advancements have enhanced efficiency, predictive accuracy, and operational reliability, they have also introduced significant ethical challenges, particularly concerning fairness and bias in decision-making. This study investigates the ethical implications of AI in critical engineering systems, focusing on fairness in resource allocation, fault detection, and safety-critical applications. Three engineering datasets were employed to examine bias and fairness in machine learning models: the KITTI Vision Benchmark Suite for autonomous vehicles, NASA’s C-MAPSS dataset for aerospace predictive maintenance, and the UCI Gas Sensor Array Drift dataset for industrial IoT applications. Ensemble methods such as Random Forest and LightGBM, known for their robustness in handling complex datasets, were used. Bias mitigation strategies, including reweighting, data augmentation, and fairness constraints, were applied to address disparities and ensure equitable outcomes. Results demonstrated substantial improvements in fairness metrics, with demographic parity, equal opportunity, and disparate impact showing an average enhancement of over 30% across datasets. Precision improved by 9.75%, recall increased by 17.71%, and AUC-ROC rose by 14.18%. Although accuracy exhibited a minor reduction (3% on average), these gains underscore the effectiveness of the mitigation techniques in achieving fairness while maintaining system performance. This study highlights the importance of integrating fairness into AI models for engineering systems, balancing performance with ethical considerations.