Self Report

Self-report, the process of individuals describing their own experiences, beliefs, or states, is a crucial data source across numerous scientific fields, but is susceptible to biases and limitations. Current research focuses on improving the reliability and validity of self-reported data through computational modeling, developing methods to mitigate biases (e.g., through gamification or careful timing of data collection), and employing machine learning techniques like BERT and deep multilabel text classification to analyze large-scale self-report datasets. These advancements aim to enhance the accuracy and utility of self-report data for applications ranging from clinical research (e.g., understanding patient experiences) to public health (e.g., tracking misinformation spread) and AI ethics (e.g., assessing potential consciousness in AI systems).