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<title>COMPAS (software)</title>

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COMPAS (software)

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</style><table class="sidebar nomobile nowraplinks plainlist"><tbody><tr><td class="sidebar-pretitle">Part of a series on</td></tr><tr><th class="sidebar-title-with-pretitle" style="font-size:180%;font-weight:bold;"><a href="Government_by_algorithm" title="Government by algorithm">Algocracy</a></th></tr><tr><td class="sidebar-image"></td></tr><tr><th class="sidebar-heading" style="background:#ddddff;">

Examples</th></tr><tr><td class="sidebar-content">

<ul><li><a href="Artificial_intelligence_in_government" title="Artificial intelligence in government">AI in government</a>

<ul><li><a href="Large_language_models_in_government" title="Large language models in government">LLMs in government</a></li></ul></li>

<li><a href="Project_Cybersyn" title="Project Cybersyn">Cybersyn</a></li>

<li><a href="Digital_dictatorship" class="mw-redirect" title="Digital dictatorship">Digital dictatorship</a></li>

<li><a href="Merit_order" title="Merit order">Merit order</a></li>

<li>Education

<ul><li><a href="Ofqual_exam_results_algorithm" title="Ofqual exam results algorithm">Ofqual exam results algorithm</a></li>

<li><a href="ChatGPT_in_education" title="ChatGPT in education">ChatGPT in education</a></li></ul></li>

<li><a href="Predictive_policing" title="Predictive policing">Predictive policing</a>

<ul><li><a href="Gangs_Matrix" title="Gangs Matrix">Gangs Matrix</a></li>

<li><a href="VioG%C3%A9n" title="VioGén">VioGén</a></li></ul></li>

<li>Predictive sentencing

<ul>

<li><a href="Offender_Assessment_System" title="Offender Assessment System">OASys</a></li>

<ul><li><a href="Robodebt_scheme" title="Robodebt scheme">Robodebt scheme</a></li>

<li><a href="Smart_city" title="Smart city">Smart city</a></li>

<li><a href="Surveillance_capitalism" title="Surveillance capitalism">Surveillance capitalism</a></li>

<li><a href="Tariffs_in_the_second_Trump_administration" title="Tariffs in the second Trump administration">Tariffs in the second Trump administration</a></li></ul>

</div></td>

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Correctional Offender Management Profiling for Alternative Sanctions (COMPAS)<a href="#cite_note-1">[1]</a> is a <a href="Legal_case_management" title="Legal case management">case management</a> and <a href="Decision_support_software" class="mw-redirect" title="Decision support software">decision support</a> software developed and owned by Northpointe (now Equivant), used by <a href="U.S._court" class="mw-redirect" title="U.S. court">U.S. courts</a> to assess the likelihood of a <a href="Defendant" title="Defendant">defendant</a> becoming a <a href="Recidivist" class="mw-redirect" title="Recidivist">recidivist</a>.<a href="#cite_note-2">[2]</a><a href="#cite_note-3">[3]</a>

COMPAS has been used by the U.S. states of New York, Wisconsin, California, Florida's <a href="Broward_County" class="mw-redirect" title="Broward County">Broward County</a>, and other jurisdictions.<a href="#cite_note-:0-4">[4]</a>

<div class="mw-heading mw-heading2"><h2 id="Risk_assessment">Risk assessment</h2></div>

The COMPAS software uses an algorithm to assess potential recidivism risk. Northpointe created risk scales for general and violent recidivism, and for pretrial misconduct. According to the COMPAS Practitioner's Guide, the scales were designed using behavioral and psychological constructs "of very high relevance to recidivism and criminal careers."<a href="#cite_note-FOOTNOTENorthpointe201527-5">[5]</a>

<dl><dt>Pretrial release risk scale</dt>

<dd>Pretrial risk is a measure of the potential for an individual to fail to appear and/or to commit new felonies while on release. According to the research that informed the creation of the scale, "current charges, pending charges, prior arrest history, previous pretrial failure, residential stability, employment status, community ties, and substance abuse" are the most significant indicators affecting pretrial risk scores.<a href="#cite_note-FOOTNOTENorthpointe201527-5">[5]</a></dd>

<dt>General recidivism scale</dt>

<dd>The General recidivism scale is designed to predict new offenses upon release, and after the COMPAS assessment is given. The scale uses an individual's criminal history and associates, drug involvement, and indications of juvenile delinquency.<a href="#cite_note-FOOTNOTENorthpointe201526-6">[6]</a></dd>

<dt>Violent recidivism scale</dt>

<dd>The violent recidivism score is meant to predict violent offenses following release. The scale uses data or indicators that include a person's "history of violence, history of non-compliance, vocational/educational problems, the person's age-at-intake and the person's age-at-first-arrest."<a href="#cite_note-FOOTNOTENorthpointe201528-7">[7]</a></dd></dl>

The violent recidivism risk scale is calculated as follows:

<msub>

<mtext>first</mtext>

</mrow>

</msub>

<msub>

<mtext>violence</mtext>

</mrow>

</msub>

<msub>

</mrow>

</msub>

<msub>

</mrow>

</msub>

</mstyle>

</mrow>

<annotation encoding="application/x-tex">{\displaystyle s=a(-w)+a_{\text{first}}(-w)+h_{\text{violence}}w+v_{\text{edu}}w+h_{\text{nc}}w}</annotation>

</semantics>

</math><img src="./3e9a7934a9822720d40e9a3f390050378a365f55.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:51.104ex; height:2.843ex;" alt="{\displaystyle s=a(-w)+a_{\text{first}}(-w)+h_{\text{violence}}w+v_{\text{edu}}w+h_{\text{nc}}w}" loading="lazy">

where <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle s}">

</mstyle>

</mrow>

<annotation encoding="application/x-tex">{\displaystyle s}</annotation>

</semantics>

</math><img src="./01d131dfd7673938b947072a13a9744fe997e632.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.09ex; height:1.676ex;" alt="{\displaystyle s}" loading="lazy"> is the violent recidivism risk score, <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle w}">

</mstyle>

</mrow>

<annotation encoding="application/x-tex">{\displaystyle w}</annotation>

</semantics>

</math><img src="./88b1e0c8e1be5ebe69d18a8010676fa42d7961e6.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.664ex; height:1.676ex;" alt="{\displaystyle w}" loading="lazy"> is a weight multiplier, <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle a}">

</mstyle>

</mrow>

<annotation encoding="application/x-tex">{\displaystyle a}</annotation>

</semantics>

</math><img src="./ffd2487510aa438433a2579450ab2b3d557e5edc.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.23ex; height:1.676ex;" alt="{\displaystyle a}" loading="lazy"> is current age, <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle a_{\text{first}}}">

<msub>

<mtext>first</mtext>

</mrow>

</msub>

</mstyle>

</mrow>

<annotation encoding="application/x-tex">{\displaystyle a_{\text{first}}}</annotation>

</semantics>

</math><img src="./6d9a077be2ae33af723cb9b73f3d6250114916dc.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:4.355ex; height:2.009ex;" alt="{\displaystyle a_{\text{first}}}" loading="lazy"> is the age at first arrest, <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle h_{\text{violence}}}">

<msub>

<mtext>violence</mtext>

</mrow>

</msub>

</mstyle>

</mrow>

<annotation encoding="application/x-tex">{\displaystyle h_{\text{violence}}}</annotation>

</semantics>

</math><img src="./48e1247cb1ebf9423f0b31c6c70aa6e61abb170f.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:7.28ex; height:2.509ex;" alt="{\displaystyle h_{\text{violence}}}" loading="lazy"> is the history of violence, <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle v_{\text{edu}}}">

<msub>

</mrow>

</msub>

</mstyle>

</mrow>

<annotation encoding="application/x-tex">{\displaystyle v_{\text{edu}}}</annotation>

</semantics>

</math><img src="./f2a467d8543282b999c15ab5d975351466fff85d.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:3.918ex; height:2.009ex;" alt="{\displaystyle v_{\text{edu}}}" loading="lazy"> is vocational education scale, and <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle h_{\text{nc}}}">

<msub>

</mrow>

</msub>

</mstyle>

</mrow>

<annotation encoding="application/x-tex">{\displaystyle h_{\text{nc}}}</annotation>

</semantics>

</math><img src="./ed59b38d244dbbe2df0b411e8d790f01f78fb8c7.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:3.215ex; height:2.509ex;" alt="{\displaystyle h_{\text{nc}}}" loading="lazy"> is history of noncompliance. The weight, <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle w}">

</mstyle>

</mrow>

<annotation encoding="application/x-tex">{\displaystyle w}</annotation>

</semantics>

</math><img src="./88b1e0c8e1be5ebe69d18a8010676fa42d7961e6.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.664ex; height:1.676ex;" alt="{\displaystyle w}" loading="lazy">, is "determined by the strength of the item's relationship to person offense recidivism that we observed in our study data."<a href="#cite_note-FOOTNOTENorthpointe201529-8">[8]</a>

<div class="mw-heading mw-heading2"><h2 id="Critiques_and_legal_rulings">Critiques and legal rulings</h2></div>

Proponents of using AI and algorithms in the courtroom tend to argue that these solutions will mitigate <a href="Cognitive_biases" class="mw-redirect" title="Cognitive biases">predictable biases and errors</a> in judges' reasoning, such as the <a href="Hungry_judge_effect" title="Hungry judge effect">hungry judge effect</a> (the phenomenon that judges are more likely to make lenient decisions after eating a meal).<a href="#cite_note-9">[9]</a>

In July 2016, the Wisconsin Supreme Court ruled that COMPAS risk scores can be considered by judges during sentencing, but there must be warnings given to the scores to represent the tool's "limitations and cautions."<a href="#cite_note-:0-4">[4]</a>

A general critique of the use of proprietary software such as COMPAS is that since the algorithms it uses are <a href="Trade_secret" title="Trade secret">trade secrets</a>, they cannot be examined by the public and affected parties which may be a violation of due process. Additionally, simple, transparent and more interpretable algorithms (such as <a href="Linear_regression" title="Linear regression">linear regression</a>) have been shown to perform predictions approximately as well as the COMPAS algorithm.<a href="#cite_note-The_Altantic-10">[10]</a><a href="#cite_note-11">[11]</a><a href="#cite_note-12">[12]</a>

Another general criticism of machine-learning based algorithms is since they are data-dependent if the data are biased, the software will likely yield biased results.<a href="#cite_note-WMD-13">[13]</a>

Specifically, COMPAS risk assessments have been argued to violate <a href="Fourteenth_Amendment_to_the_United_States_Constitution" title="Fourteenth Amendment to the United States Constitution">14th Amendment</a> <a href="Equal_Protection" class="mw-redirect" title="Equal Protection">Equal Protection</a> rights on the basis of race, since the algorithms are argued to be racially discriminatory, to result in disparate treatment, and to not be narrowly tailored.<a href="#cite_note-14">[14]</a>

<div class="mw-heading mw-heading2"><h2 id="Accuracy">Accuracy</h2></div>

In 2016, <a href="Julia_Angwin" title="Julia Angwin">Julia Angwin</a> was co-author of a <a href="ProPublica" title="ProPublica">ProPublica</a> investigation of the algorithm.<a href="#cite_note-ProPublica-15">[15]</a> The team found that "blacks are almost twice as likely as whites to be labeled a higher risk but not actually re-offend," whereas COMPAS "makes the opposite mistake among whites: They are much more likely than blacks to be labeled lower-risk but go on to commit other crimes."<a href="#cite_note-ProPublica-15">[15]</a><a href="#cite_note-The_Altantic-10">[10]</a><a href="#cite_note-New_York_Times-16">[16]</a> They also found that only 20 percent of people predicted to commit violent crimes actually went on to do so.<a href="#cite_note-ProPublica-15">[15]</a>

In a letter, Northpointe criticized ProPublica's methodology and stated that: "[The company] does not agree that the results of your analysis, or the claims being made based upon that analysis, are correct or that they accurately reflect the outcomes from the application of the model."<a href="#cite_note-ProPublica-15">[15]</a>

Another team at the <a href="Community_Resources_for_Justice" title="Community Resources for Justice">Community Resources for Justice</a>, a criminal justice <a href="Think_tank" title="Think tank">think tank</a>, published a rebuttal of the investigation's findings.<a href="#cite_note-CRJ-17">[17]</a> Among several objections, the CRJ rebuttal concluded that the Propublica's results: "contradict several comprehensive existing studies concluding that actuarial risk can be predicted free of racial and/or gender bias."<a href="#cite_note-CRJ-17">[17]</a>

A subsequent study has shown that COMPAS software is somewhat more accurate than individuals with little or no criminal justice expertise, yet less accurate than groups of such individuals.<a href="#cite_note-Science_Advances-18">[18]</a> They found that: "On average, they got the right answer 63 percent of their time, and the group's accuracy rose to 67 percent if their answers were pooled. COMPAS, by contrast, has an accuracy of 65 percent.".<a href="#cite_note-The_Altantic-10">[10]</a> Researchers from the <a href="University_of_Houston" title="University of Houston">University of Houston</a> found that COMPAS does not conform to <a href="Fairness_(machine_learning)" title="Fairness (machine learning)">group fairness</a> criteria and produces various kinds of unfair outcomes across sex- and race-based demographic groups.<a href="#cite_note-19">[19]</a> In 2024, a group of researchers from <a href="Williams_College" title="Williams College">Williams College</a> found that the use of <abbr>COMPAS</abbr> in <a href="Broward_County%2C_Florida" title="Broward County, Florida">Broward County</a> led to a reduced rate of confinement across demographic groups, but that the use of the algorithm exacerbated differences in confinement between racial groups, thereby deepening racial disparity.<a href="#cite_note-20">[20]</a>

<ul><li><a href="Algorithmic_bias" title="Algorithmic bias">Algorithmic bias</a></li>

<li><a href="Garbage_in%2C_garbage_out" title="Garbage in, garbage out">Garbage in, garbage out</a></li>

<li><a href="Legal_expert_systems" class="mw-redirect" title="Legal expert systems">Legal expert systems</a></li>

<li><a href="Loomis_v._Wisconsin" title="Loomis v. Wisconsin">Loomis v. Wisconsin</a></li>

<li><a href="Criminal_sentencing_in_the_United_States" title="Criminal sentencing in the United States">Criminal sentencing in the United States</a></li></ul>

<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

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<div class="mw-heading mw-heading2"><h2 id="Further_reading">Further reading</h2></div>

<ul><li><cite id="CITEREFNorthpointe2015" class="citation web cs1">Northpointe (March 15, 2015). <a rel="nofollow" class="external text" href="https://assets.documentcloud.org/documents/2840784/Practitioner-s-Guide-to-COMPAS-Core.pdf">"A Practitioner's Guide to COMPAS Core"</a> (PDF).</cite></li>

<li><cite id="CITEREFAngwinLarson2016" class="citation journal cs1">Angwin, Julia; Larson, Jeff (May 23, 2016). <a rel="nofollow" class="external text" href="https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing">"Machine Bias"</a>. ProPublica. Retrieved November 21, 2019.</cite></li>

<li><cite id="CITEREFFloresLowenkampBechtel" class="citation web cs1">Flores, Anthony; Lowenkamp, Christopher; Bechtel, Kristin. <a rel="nofollow" class="external text" href="http://www.crj.org/assets/2017/07/9_Machine_bias_rejoinder.pdf">"False Positives, False Negatives, and False Analyses"</a> (PDF). Community Resources for Justice. Retrieved November 21, 2019.</cite></li>

<li><a rel="nofollow" class="external text" href="https://www.documentcloud.org/documents/2702103-Sample-Risk-Assessment-COMPAS-CORE.html">Sample COMPAS Risk Assessment</a></li></ul></div><div><div class="zim-footer">

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