⚖️ Algorithmic-Fairness-Analysis-in-Employee-Promotion-Models
📌 Overview
This project investigates fairness and bias in machine learning models used for employee promotion decisions. Using a real-world HR dataset, the goal is to evaluate whether predictive models produce equitable outcomes across demographic groups—and to explore methods for mitigating bias while maintaining model performance.
The work emphasizes a key challenge in modern AI systems: balancing predictive accuracy with fairness and ethical responsibility.
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🎯 Objectives • Predict whether an employee is promoted based on historical data • Measure fairness using standard bias metrics • Identify disparities across demographic groups • Apply bias mitigation techniques and evaluate tradeoffs
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📊 Dataset • ~54,000 employee records • Features include: • Education level • Years of service • Training scores • Previous performance • Department and region • Demographic attributes (used for fairness evaluation)
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⚙️ Methodology
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Data Preprocessing • Cleaned missing and inconsistent values • Encoded categorical variables • Split data into training/testing sets
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Model Development • Implemented Logistic Regression as a baseline classifier • Trained model to predict promotion outcomes
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Fairness Evaluation
Measured bias using: • Statistical Parity Difference (SPD) → Difference in positive prediction rates between groups • Equal Opportunity Difference (EOD) → Difference in true positive rates between groups
These metrics quantify whether certain groups are systematically favored or disadvantaged.
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🛠️ Bias Mitigation
Applied reweighing techniques to reduce bias in training data by adjusting sample importance across groups.
Evaluated how mitigation impacts: • Model accuracy • Precision and recall • Fairness metrics (SPD, EOD)
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📈 Results • Baseline model achieved strong predictive performance • Detectable bias was present across demographic groups • Reweighing reduced bias (improved SPD/EOD) • However, fairness improvements came with tradeoffs in accuracy
👉 Key takeaway:
Improving fairness often reduces predictive performance—highlighting the need for careful, context-dependent decision-making in real-world systems.
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🧠 Key Insights • Bias can persist even when sensitive attributes are not explicitly used • Fairness must be measured explicitly, not assumed • There is no single “best” model—only tradeoffs between competing objectives • Ethical AI requires both technical methods and human judgment