Ethical AI in Data Engineering: Mitigating Bias in Data-Driven Decision-Making

Abstract

Bias in AI models can lead to unfair or discriminatory outcomes, creating significant challenges for organizations implementing data-driven decision-making systems across industries. This comprehensive review synthesizes current methodologies for integrating ethical AI principles into data engineering processes to detect, measure, and mitigate biases in data pipelines and machine learning models. The review focuses on three critical areas: bias detection algorithms that identify unfair patterns in data and models, fairness-aware data preprocessing techniques that remediate biased datasets before model training, and governance frameworks that provide organizational structures for implementing ethical AI practices at scale. As organizations increasingly rely on AI-driven decision-making systems, addressing algorithmic bias has become essential for ensuring equitable outcomes across diverse populations. The review demonstrates that preprocessing interventions can substantially reduce discriminatory outcomes while maintaining model accuracy within acceptable performance ranges. Contemporary implementations reveal that comprehensive bias detection frameworks require additional computational overhead above baseline model training costs, with processing times varying significantly based on dataset size and complexity. Organizations implementing dedicated ethical AI roles experience 67% fewer compliance violations compared to those relying on distributed responsibility models. IBM's AI Ethics Board across 147 projects achieved 89% reduction in post-deployment ethical issues with 78% project approval rate and average 12.4 days decision timeline. The integration of ethical AI principles represents both a moral imperative and a practical necessity for responsible artificial intelligence deployment.