Bias Detection in Artificial Intelligence Models and Data Engineering for AI Systems With the rapid expansion of Artificial Intelligence applications across various sectors, ensuring fairness and reliability has become a critical priority. Bias detection in AI models represents one of the most significant scientific and ethical challenges in modern AI systems. AI models may produce unfair outcomes when trained on historically biased or imbalanced datasets. Such bias is frequently observed in hiring systems, credit scoring applications, and facial recognition technologies, where demographic disparities may unintentionally influence decisions. Bias does not originate solely from algorithms but primarily from the data used to train them. If datasets reflect social or structural imbalances, models may replicate and even amplify those patterns. Detecting bias therefore requires statistical fairness metrics such as equal opportunity, demographic parity, and cross-group performance evaluation. Explainable AI techniques are also employed to interpret model decisions, enhancing transparency and enabling researchers to identify potential distortions. Meanwhile, data engineering for AI systems serves as the foundation for building accurate and robust models. Data engineering encompasses data collection, cleaning, preprocessing, transformation, storage, and preparation for machine learning workflows. High-quality, well-structured data significantly improves model performance and reduces the risk of misleading outcomes. Poor data quality, inconsistencies, or missing values can severely undermine even the most advanced algorithms. Modern data engineering relies on automated data pipelines, scalable database systems, and continuous data quality monitoring tools. Proper documentation of data sources and preprocessing methods further strengthens accountability and governance. By integrating strong data engineering practices with systematic bias detection mechanisms, AI systems can become more transparent, equitable, and dependable. The synergy between bias detection methodologies and advanced data engineering is not merely a technical improvement but an academic and ethical obligation to ensure responsible and sustainable AI development. Al-Mustaqbal University is ranked first among Iraqi private universities.