## 5 Fair Training
### Post-Processing Fairness  
Post-processing fairness methods focus on adjusting or post-processing the output of trained AI models to ensure fairness and reduce bias after the model has made its predictions. These techniques are applied after the model has made a decision to align the results with the fairness goal and mitigate any potential bias present in the predictions. Some common post-processing fairness methods include equalized odds post-processing [11; 144; 177; 180], calibration post-processing [187; 188; 189], rejected options classification (ROC) post-processing [191; 144; 192; 193], priority sampling post-processing [194; 195; 196], threshold optimization post-processing [197; 198; 199; 200], and regularization post-processing [201; 202; 203; 204]. Table 8 summarizes the features, pros and cons with the comparison of these methods.  
Equalized odds post-processing is employed post-model training to align predictions, ensuring equal false alarm and omission rates across different groups. Calibration refines predicted probabilities to accurately reflect event likelihood. Reject option classification introduces a \"reject\" option to avoid biased predictions in sensitive situations. Preferential sampling post-processing reshapes training data distribution for enhanced fairness. Threshold optimization post-processing adjusts decision thresholds for a balanced fairness-accuracy trade-off. Regularization post-processing employs regularization techniques to encourage fairness during model optimization. These techniques offer ways to enhance fairness in AI predictions and are particularly useful in contexts like credit scoring and hiring decisions.  
The effectiveness and suitability of post-processing fairness methods vary with the AI application and fairness goals. While valuable in certain contexts, these techniques might not entirely resolve root biases. A holistic AI fairness approach should combine pre-processing, in-processing, and post-processing methods, alongside continuous monitoring and evaluation, to ensure equitable outcomes.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**In-Processing Fairness Method** & **Features** & **Pros** & **Cons** & **References** \\ \hline Adversarial Training & Adversarial component to minimize bias impact & Enhances model’s fairness while maintaining accuracy & Sensitive to adversarial attacks, requires additional computational resources & [172; 173; 174; 175; 176] \\ \hline Adversarial Debiasing & Adversarial network to remove sensitive attributes & Simultaneously reduces bias and improves model’s fairness & Adversarial training challenges, potential loss of predictive performance & [177; 178; 179; 137] \\ \hline Equalized Odds Post-processing & Adjust model predictions to ensure equalized odds & Guarantees fairness in binary classification tasks & May lead to suboptimal trade-offs between fairness and model performance & [11; 177; 180] \\ \hline Causal Learning for Fairness & Focus on causal relationships to adjust for bias & Addresses confounding factors to achieve fairer predictions & Requires causal assumptions, may be limited by data availability & [181; 182; 183; 45; 184] \\ \hline Meta Fairness & Learns fairness-aware optimization algorithm & Adapts fairness-accuracy trade-off to changing requirements & Complexity in learning the optimization algorithm, potential increased complexity & [163; 185; 186] \\ \hline \hline \end{tabular}
\end{table}
Table 7: In-processing fairness comparison.