AI Ethics in Engineering: Enhancing Fairness in Machine Learning Models for Critical Systems

The integration of artificial intelligence (AI) and machine learning (ML) into engineering systems has revolutionized industries such as autonomous vehicles, renewable energy management, and industrial automation. While these advancements have significantly improved efficiency, predictive accuracy, and operational reliability, they have also introduced 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 analyzed to evaluate bias and fairness in ML 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 were employed due to their robustness in handling complex datasets. Bias mitigation strategies, including reweighting, data augmentation, and fairness constraints, were applied to address disparities and ensure equitable outcomes. Results demonstrated significant 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 experienced 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 to ensure equitable and reliable outcomes in critical applications.