# Autism Screening in Toddlers and Adults Using Deep Learning and Fair AI Techniques
## 2 Materials and Methods
### AI Fairness Techniques
Step 3: Each particle updates the position with respect to the current position, current velocity, the distance between the current position and local position, and the distance between the current position and global position. PSO is used extensively for concurrent processing and requires very few algorithm parameters. It is an efficient global search algorithm that is free of derivatives. * Optimization using Adam: The Adam optimizer improves the Stochastic Gradient Descent for updating network weights iteratively during the learning process. It is easy to implement and computationally efficient. It requires little training for the hyperparameters and does not need a lot of memory to run. Adam incorporates the Adaptive Gradient Algorithm (AdaGrad) and the Root Mean Square Propagation (RMSProp). AdaGrad is responsible for maintaining a pre-parameter learning rate to reduce error and improve overall performance. In contrast, RMSProp maintains the learning rates based on an average of recent magnitudes of weights. Adam uses an average of second moments of the gradient and calculates the exponential moving average of the gradient and squared gradient. It also takes into account parameters that are essential for controlling decay rates (beta1 and beta2). Adam has several parameters: Alpha: alpha is the step size or learning rate. Larger values lead to faster learning before rate updation and smaller values lead to slow learning. Beta1: exponential decay rate (first moment) Beta2: exponential decay rate (second moment) Epsilon: epsilon denotes a minimal number for preventing any division by zero Apart from the techniques mentioned above that have been used in the analysis, some of the other fair AI techniques have been mentioned as follows:
* Focal Loss: Focal Loss functions and an extension of cross-entropy are used for handling class imbalance problems by applying a modulating term to the class entropy loss. More weights are assigned to hard or easily misclassified images, which could be imaged with noisy data, partial images, or background images. As the loss contribution from easy examples is reduced, misclassified images get corrected. It is a method of dynamically scaling the cross-entropy loss such that the scaling factor decays to zero. This leads to an increase in confidence in the correct classes. The scaling factor hence weights the impact of easy examples while training, ensuring that the model is focused on hard examples;
* Focal Tversky Loss: Neural networks often deploy Tversky loss for image segmentation problems. It is a loss function that is used to handle an imbalance in images, specifically when the number of positive pixels is orders of magnitude smaller than the number of negative pixels. The function is capable of comparing and predicting the outputs of neural networks with the true output. For image segmentation problems, the segmented image acts as the predicted output, whereas the ground truth segmentation of the same image is the true output. Unlike other loss functions like binary cross-entropy loss and dice coefficient, the Tversky loss allows more control over the relationship between precision and recall. It is possible to penalize false negatives more heavily than false positives, and vice versa by adjusting the hyperparameters. Focal Tversky loss is more like a generalized Tversky loss that applies the concept of focal loss for focusing on hard cases with low probabilities.