Semantic Scholar Open Access 2018 1012 sitasi

The accuracy, fairness, and limits of predicting recidivism

Julia Dressel Hany Farid

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Abstrak

Should we trust computers to make life-altering decisions in the criminal justice system? Algorithms for predicting recidivism are commonly used to assess a criminal defendant’s likelihood of committing a crime. These predictions are used in pretrial, parole, and sentencing decisions. Proponents of these systems argue that big data and advanced machine learning make these analyses more accurate and less biased than humans. We show, however, that the widely used commercial risk assessment software COMPAS is no more accurate or fair than predictions made by people with little or no criminal justice expertise. In addition, despite COMPAS’s collection of 137 features, the same accuracy can be achieved with a simple linear classifier with only two features.

Topik & Kata Kunci

Medicine Computer Science

Penulis (2)

Julia Dressel

Hany Farid

Format Sitasi

APA MLA BibTeX

Dressel, J., Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. https://doi.org/10.1126/sciadv.aao5580

Akses Cepat

Lihat di Sumber doi.org/10.1126/sciadv.aao5580

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 1012×
Sumber Database: Semantic Scholar
DOI: 10.1126/sciadv.aao5580
Akses: Open Access ✓