## 5 Fair Training
### Counterfactual Fairness  
Counterfactual fairness is an approach that seeks to address bias in AI models by considering counterfactual scenarios, where sensitive attributes are altered while keeping other features fixed. The idea is to evaluate fairness by examining how the model's predictions would change if an individual belonged to a different demographic group, allowing for a more nuanced understanding of biases. Table 10 summarizes and compares different regularization methodologies for AI fairness, including individual fairness [40; 168; 196; 219], equal opportunity fairness [220; 221; 222], reweighted counterfactual fairness [223; 224; 225], and oblivious training [226; 227; 228]. Individual fairness focuses on treating similar individuals equally despite their protected attributes. It promotes personalized fairness at the individual level, emphasizing fine-grained treatment. Equal opportunity fairness ensures similar true positive rates across different groups to prevent disparate impact in binary classification. Reweighted counterfactual fairness adjusts data weights during training to mitigate bias and can be combined with fairness-aware algorithms. Oblivious training trains models on both original and counterfactual data to promote fairness without explicit labels. These methods address different fairness concerns, considering both individual and group fairness aspects, each with their computational and implementation considerations. Each fairness method has strengths and limitations, potentially impacting areas like model performance and interpretability. The method chosen should align with specific fairness \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Counterfactual Fairness Method** & **Features** & **Pros** & **Cons** & **References** \\ \hline Individual Fairness & Focuses on treating similar individuals similarly based on their features & Considers fairness at the individual level, promoting personalized fairness fairness & Defining similarity metrics and enforcing individual fairness can be challenging & [40; 196; 219] \\ \hline Equal Opportunity Fairness & Minimizes disparate impact on true positive rates across sensitive attribute groups & Targets fairness in favor of historically disadvantaged groups & May neglect other fairness concerns, such as false positive rates or overall accuracy & [220; 221; 222] \\ \hline Equalized Odds Fairness & Aims for similar false positive and true positive rates across sensitive attribute groups & Addresses fairness in both false positives and false negatives & May lead to accuracy trade-offs between groups & [229; 220; 221; 223; 224; 225; 226; 227; 228; 229; 230; 231] \\ \hline Reweighted Counterfactual Fairness & Assigns different weights to instances based on similarity to counterfactual scenarios & Provides better fairness control by adjusting instance weights & Determining appropriate weights and balancing fairness and accuracy can be challenging & [223; 224; 225] \\ \hline Oblivious Training & Trains the model to be ignorant of certain sensitive attributes during learning & Offers a simple and effective way to mitigate the impact of sensitive attributes are relevant to the task & May result in lower model performance when sensitive attributes are relevant to the task & [226; 227; 228] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Counterfactual fairness methods comparison. criteria and application contexts, as certain methods may be better suited for particular domains than others. In medical data collection, informed consent methods are employed to clarify data usage and potential risks. Privacy techniques like anonymization protect individuals' identities. Data minimization reduces privacy risks by collecting only necessary information, though this may limit insights. Transparency communicates data collection processes, building trust while potentially raising privacy concerns. Data security measures include encryption and access controls to prevent unauthorized access.