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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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Related Experiment Video

Updated: Jan 27, 2026

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The sexist algorithm.

Melissa Hamilton1

  • 1University of Surrey School of Law, Guildford, United Kingdom.

Behavioral Sciences & the Law
|April 2, 2019
PubMed
Summary

Algorithmic risk assessment tools like COMPAS show gender bias. These tools systematically overclassify women as higher risk, despite lower recidivism rates, raising concerns about fairness in criminal justice.

Area of Science:

  • Criminal Justice
  • Data Science
  • Sociology

Background:

  • Algorithmic risk assessment tools are increasingly used in criminal justice to support evidence-based practices.
  • These tools aim to objectively predict offender recidivism, informing management decisions.
  • Concerns exist regarding the fairness and equity of these automated assessments, particularly concerning gender.

Purpose of the Study:

  • To investigate the gender equity of the COMPAS risk assessment tool.
  • To assess the predictive parity of COMPAS outcomes across genders.
  • To determine if algorithmic risk assessments exhibit bias against women.

Main Methods:

  • Utilized a large dataset of offenders scored using the COMPAS risk assessment tool.
  • Analyzed COMPAS performance in discriminating between recidivists and non-recidivists for both men and women.

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  • Applied multiple measures of algorithmic equity and predictive accuracy.

    • Main Results:

    • COMPAS demonstrated reasonable accuracy in predicting recidivism for both men and women.
    • Algorithmic outcomes from COMPAS systematically overclassified women into higher risk categories.
    • Discrepancies in risk classification between genders were observed across various equity and accuracy metrics.

    Conclusions:

    • While COMPAS shows predictive capability, its application reveals systemic gender bias.
    • The algorithm's tendency to overclassify women suggests it is not equitable.
    • This research indicates that the COMPAS algorithm exhibits sexist tendencies in its risk assessments.