Using data from 1946–2014, we show that audio features of lawyers’ introductory statements and lawyers’ facial attributes improve the performance of the best prediction models of Supreme Court outcomes. We infer face attributes using the MIT-CBCL human-labeled face database and infer voice attributes using a 15-year sample of human-labeled Supreme Court advocate voices. We find that image features improved prediction of case outcomes from 63.8% to 65.6%, audio features improved prediction of case outcomes from 66.8% to 68.8%, image and audio features together improved prediction of case outcomes from 66.9% to 67.7%, and the weights on lawyer traits are approximately half the weight of the most important feature from the models without image or audio features. Predictions of Justice votes with image and/or audio features however remained more similar relative to their baselines. We interpret this difference to suggest that human biases are more relevant in close cases.
Computational Analysis of Law, 2019, forthcoming