Recidivism and Peer Influence with LLM Text Embeddings in Low Security Correctional Facilities
This work addresses recidivism prediction and social dynamics in correctional facilities, with incremental methodological improvements for peer effect analysis.
The study tackled predicting recidivism using LLM text embeddings from written exchanges in low-security correctional facilities, achieving a 30% higher accuracy compared to pre-entry covariates, and found significant peer effects in language usage through new peer effect estimation methods.
We find AI embeddings obtained using a pre-trained transformer-based Large Language Model (LLM) of 80,000-120,000 written affirmations and correction exchanges among residents in low-security correctional facilities to be highly predictive of recidivism. The prediction accuracy is 30\% higher with embedding vectors than with only pre-entry covariates. However, since the text embedding vectors are high-dimensional, we perform Zero-Shot classification of these texts to a low-dimensional vector of user-defined classes to aid interpretation while retaining the predictive power. To shed light on the social dynamics inside the correctional facilities, we estimate peer effects in these LLM-generated numerical representations of language with a multivariate peer effect model, adjusting for network endogeneity. We develop new methodology and theory for peer effect estimation that accommodate sparse networks, multivariate latent variables, and correlated multivariate outcomes. With these new methods, we find significant peer effects in language usage for interaction and feedback.