Is Machine Learning Unsafe and Irresponsible in Social Sciences? Paradoxes and Reconsidering from Recidivism Prediction Tasks

Initially, those scholars employ these historical elements to forecast whether the criminal would re-offend. Subsequently, the binary outcome of recidivism serves as a proxy variable for recidivism risk. Some computer scientists also employ the probability (or score) assigned by the model for an offender's likelihood of re-offense as a gauge for their recidivism risk (Etzler et al., 2023; Ma et al., 2022; Wang et al., 2022). While such configurations may seem intuitively compelling, they often embody an oversimplified and deterministic viewpoint, which stands in contradiction to contemporary social science theories. Firstly, historical factors alone are insufficient predictors of human actions.