Adult Income Classification and Fairness Model
Model Details
This educational project predicts Class-label = 1 in the cleaned Adult dataset and compares Random Forest, Multinomial Naive Bayes, k-nearest neighbors, and Linear SVM.
- Selected model: Random Forest
- Framework: scikit-learn 1.6.1
- Training timestamp: 2026-08-03T10:36:46.709070+00:00
- Sensitive attribute evaluated: gender
Intended Use
Responsible AI education, model-card demonstrations, and classroom experiments with predictive performance and group fairness.
Out-of-Scope Use
Do not use this model for employment, lending, insurance, housing, healthcare, criminal-justice, or other consequential decisions.
Dataset
- Rows: 45,222
- Input features: 14
- Target:
Class-label
- Positive class:
1
- Protected groups evaluated:
Male and Female
Preprocessing
- Stratified 80/20 train/test split
- Median imputation and min-max scaling for numeric columns
- Most-frequent imputation and one-hot encoding for categorical columns
Evaluation Results
| Model | Accuracy | Male positive rate | Female positive rate | Statistical parity difference | Disparate impact (Female/Male) |
|---|
| Random Forest | 0.8515 | 0.2707 | 0.0811 | 0.1896 | 0.2995 |
| Linear SVM | 0.8452 | 0.2541 | 0.0744 | 0.1797 | 0.2928 |
| k-NN | 0.8262 | 0.2803 | 0.0814 | 0.1989 | 0.2905 |
| Naive Bayes | 0.7925 | 0.4537 | 0.0640 | 0.3897 | 0.1411 |
Fairness Evaluation
Statistical parity difference: P(predicted positive | Male) - P(predicted positive | Female).
A value near zero indicates similar positive-prediction rates. Disparate impact is reported as the female rate divided by the male rate.
Limitations and Risks
- Statistical parity is only one group-fairness metric and does not prove overall fairness.
- The dataset uses binary gender categories and does not represent all gender identities.
- Historical data may encode structural inequalities.
- Accuracy can hide unequal false-positive and false-negative rates.
- No causal, intersectional, robustness, calibration, or post-deployment analysis was performed.
Recommended Further Evaluation
Evaluate equal opportunity, equalized odds, calibration, subgroup confusion matrices, intersectional groups, robustness, stakeholder-defined harms, and post-deployment monitoring.
Reproducibility
The complete pipeline is stored in adult_income_best_model.joblib, and the comparison is stored in model_comparison.csv.
Environmental Impact
These are relatively small CPU-based tabular models. No formal energy or carbon measurement was conducted.
