Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
January 5, 2018Science AdvancesOpen Access

The accuracy, fairness, and limits of predicting recidivism

View Full Paper
Ask AI
Bookmark
Share

Authors

JDJulia DresselDartmouth CollegeHFHany FaridUniversity of California, Berkeley

Discussion

Loading...

Member takes

Implication

Randomized trial shows no improvement in recidivism predictions by algorithms compared to human judgment, suggesting limitations.

Key Points

  • This research aims to evaluate the accuracy and fairness of recidivism prediction algorithms compared to human judgment.
  • Analyzed predictions from the COMPAS software and compared it to predictions made by individuals with minimal criminal justice expertise.
  • Developed a simple linear predictor using only two features to compare accuracy with COMPAS.
  • COMPAS did not show improved accuracy or fairness compared to predictions made by non-experts.
  • The simple linear predictor was found to be nearly equivalent to COMPAS despite utilizing only two features.

Cite This Study

Dressel et al. (2018) studied this question.

synapsesocial.com/papers/69fc580c2bd1d6a2a7dd9b09https://doi.org/10.1126/sciadv.aao5580
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Predicting relapse: A meta-analysis of sexual offender recidivism studies.1998 · 213 citations
  2. 2The Regression Analysis of Binary Sequences1958 · 2,859 citations
  3. 3The Robust Beauty of Majority Rules in Group Decisions.2005 · 484 citations
  4. 4The Relationship Between Static and Dynamic Risk Factors and Reconviction in a Sample of U.K. Child Abusers2002 · 220 citations