Fairness in Deep Neural Networks Through Dropout Techniques

This work asks several key questions about improving Fairness in deep neural networks (DNNS) using Dropout techniques:

1. How effective are Randomized Algorithms in enhancing fairness of DNNs?
2. Can dropout strategies improve both fairness and utility at the same time?
3. What design factors should be considered for search algorithms that aim at improving fairness through dropout methods?
4. How do dropout strategies perform compared to advanced methods used to reduce bias?

#Datasets and Models

To answer these questions, five different datasets known for fairness issues are analyzed. In some cases, two different protected groups are looked at, leading to a total of seven comparisons. The datasets include:

• Adult Census Income: This looks at predicting if an individual's income exceeds a specific threshold.
• Bank Marketing: This examines whether a person is likely to subscribe to a product.
• Compas Software: This checks if an individual will re-offend.
• Default Credit: This predicts if a person will default on a credit card payment.
• Medical Expenditure (MEPS16): This assesses whether someone will use medical benefits.

Models based on deep neural networks were trained on these datasets. Each dataset is matched with a specific DNN architecture, and different dropout rates were applied during training to test their effects on fairness and performance.

#Technical Details

The experiments were conducted on a desktop computer equipped with an Intel Core i7 processor, sufficient RAM, and a hard disk drive. The machine learning framework and libraries used for the implementation included Python and others for numerical and machine learning tasks.

During the experiments, performance was evaluated using different random seeds to ensure the findings are reliable and not just a result of one specific training run. The training, validation, and test data were selected randomly, and the models were trained using specific techniques to maintain their accuracy.

#Experimental Setup

For each dataset, the models were trained using a set proportion of data for training, validation, and testing. The primary aim during training was to balance the model's performance while ensuring fairness across protected groups. Hyperparameters were carefully tuned, including the thresholds for acceptable fairness improvements.

#Effectiveness of Randomized Algorithms in Fairness Mitigation

The first research question examines if randomized algorithms can effectively reduce bias through dropout techniques. The findings showed a steady improvement in fairness over time as the algorithms searched for better subsets of neurons to deactivate.

Overall, the randomized strategies managed to improve fairness in a significant number of cases, with both algorithms showing encouraging results. However, one algorithm, known as simulated annealing (SA), performed better than the random walk (RW) algorithm across various datasets. In some cases, fairness improved to nearly 69%.

#Improving Fairness and Utility

The second question looks at whether we can enhance both fairness and utility. Typically, as fairness improves, other metrics like the F1 score tend to decline, which suggests a trade-off between fairness and model utility. However, in some datasets, accuracy did improve while achieving greater fairness.

The datasets often featured an imbalance of classes, which affected how accuracy and F1 scores aligned. As more neurons were dropped out, the models tended to predict negatives more often, leading to improved overall accuracy despite a drop in the F1 score.

#Hyperparameters of Randomized Algorithms

The algorithms utilized several hyperparameters, which included multipliers for F1 thresholds and limits for neuron dropout. Adjusting these parameters can have significant effects on both fairness and model performance.

In particular, reducing the threshold for F1 scores can positively influence fairness. Increasing the number of neurons dropped and extending the search time can also lead to better outcomes. Fine-tuning these hyperparameters allows for a more tailored approach to improving fairness in models.

#Comparing to State-of-the-Art Techniques

The final research question assesses how these dropout strategies stack up against advanced post-processing techniques designed to reduce bias. One such method involves targeting specific neurons that significantly influence fairness. This method's effectiveness is limited as it assesses only one neuron at a time, while the dropout techniques can evaluate multiple neurons simultaneously.

The findings show that the dropout method outperformed this advanced technique in improving fairness across all datasets examined. The advantage, especially notable in certain datasets, where the difference in fairness improvement was significant, was clear.

#Conclusions

In summary, randomized algorithms show great promise in improving fairness in DNNs through dropout techniques. Although enhancing fairness often comes at a cost to other performance metrics like the F1 score, some strategies can help balance these aspects.

The findings suggest that with well-tuned hyperparameters and thoughtful design considerations, it is indeed possible to work towards fairer models without sacrificing too much in terms of utility. The ability of these methods to outperform existing state-of-the-art techniques in fairness improvement highlights their effectiveness.

Moving forward, researchers can explore further into the interplay between fairness, utility, and the parameters that influence randomized algorithms. By honing in on these aspects, the path lies clear for generating more inclusive and equitable machine learning models.