The limits of human predictions of recidivism.

Dressel and Farid recently found that laypeople were as accurate as statistical algorithms in predicting whether a defendant would reoffend, casting doubt on the value of risk assessment tools in the criminal justice system. We report the results of a replication and extension of Dressel and Farid's experiment. Under conditions similar to the original study, we found nearly identical results, with humans and algorithms performing comparably. However, algorithms beat humans in the three other datasets we examined. The performance gap between humans and algorithms was particularly pronounced when, in a departure from the original study, participants were not provided with immediate feedback on the accuracy of their responses. Algorithms also outperformed humans when the information provided for predictions included an enriched (versus restricted) set of risk factors. These results suggest that algorithms can outperform human predictions of recidivism in ecologically valid settings

Towards A New Paradigm in Psychiatry

The reductionist tenets of the biomedical model of mental illness generate research methods and clinical practices that neglect significant cultural elements of mental illness. The biomedical model is reductionist because it assumes a view of the mind that lends itself to biological reductionism. Developing a more holistic model of mental illness requires replacing the accepted view of mind with a new one. In this paper, research demonstrating the significance of culture to mental illness will be reviewed in order to illuminate the flaws of the biomedical model. The extended mind theory will be analyzed and discussed as a potential basis for the development of a new paradigm within psychiatry, one which transcends the reductionist tendencies of the biomedical model