Initial Guessing Bias: How Untrained Networks Favor Some Classes

Understanding and controlling biasing effects in neural networks is crucial for ensuring accurate and fair model performance. In the context of classification problems, we provide a theoretical analysis demonstrating that the structure of a deep neural network (DNN) can condition the model to assign all predictions to the same class, even before the beginning of training, and in the absence of explicit biases. We prove that, besides dataset properties, the presence of this phenomenon, which we call \textit{Initial Guessing Bias} …

analysis bias class classification cond-mat.dis-nn context cs.lg deep neural network dnn effects fair network networks neural network neural networks performance predictions stat.ml understanding