Unmasking AI: My Mission to Protect What Is Human in a World of Machines

by Joy Buolamwini

To be useful, a neural network must be trained to respond to a specific kind of pattern. The training process of a neural network strengthens some connections and weakens others so the trained neural network model can recognize a pattern. Researchers have developed different kinds of training methods to create machine learning models, which are neural networks configured to recognize a specific pattern. In general, the components of machine learning involve training data, testing data, a neural network to configure, and a learning algorithm to build the experience of the neural network. Remember, an algorithm is a sequence of instructions used to achieve a specific outcome. The goal of a learning algorithm for a neural network is to set the weights between the nodes in the best possible way to recognize a pattern. In the case of detecting an object like a car in an image, the neural network is exposed to many training images that contain a car and the weights are refined many times until the model can consistently detect cars in images it was not previously exposed to.