Audiovisual Methods for Empirical Legal Studies

While recent advances in machine learning and especially text analysis have already transformed empirical legal scholarship, previous work has mostly ignored some of the richest sources of legal data: images and audio. This article contributes to reducing this gap by introducing empirical legal scholars to computer-vision and computational-audio techniques that can be applied to the empirical legal domain. These techniques enable descriptive, causal, and predictive studies that were previously impossible due to scale and computational complexity. After reviewing general approaches to audio and visual machine learning, we illustrate the usefulness of these methods on a sample of videos from the United States Ninth Circuit Court of Appeals.