Just Algorithms: Using Science To Reduce Incarceration And Inform A Jurisprudence Of Risk

183 Pages·2021·1.235 MB·English

by  Christopher Slobogin

just algorithms
Statistically-derived algorithms, adopted by many jurisdictions in an effort to
identify the risk of reoffending posed by criminal defendants, have been
lambasted as racist, de-humanizing, and antithetical to the foundational tenets
of criminal justice. Just Algorithms argues that these attacks are misguided and
that,properlyregulated,riskassessmenttoolscanbeacrucialmeansofsafelyand
humanely dismantling our massive jail and prison complex. The book explains
howriskalgorithmswork,thetypesoflegalquestionstheyshouldanswer,andthe
criteriaforjudgingwhethertheydosoinawaythatminimizesbiasandrespects
humandignity.Italsoshowshowriskassessmentinstrumentscanprovideleverage
for curtailing draconian prison sentences and the plea-bargaining system that
produces them. The ultimate goal of Christopher Slobogin’s insightful analysis
istodeveloptheprinciplesthatshouldgovern,inboththepretrialandsentencing
settings,thecriminaljusticesystem’sconsiderationofrisk.
Christopher Slobogin holds the Milton Underwood Chair at Vanderbilt
University Law School. He has authored or co-authored eight books and over
150 articles on criminal justice issues. He is one of the most heavily cited law
professorsinthecriminaljusticefieldandistheonlylawprofessortohavereceived
Distinguished Scholar awards from both the American Psychology-Law Society
andtheAmericanBoardofForensicPsychology.

Just Algorithms
using science to reduce incarceration
and inform a jurisprudence of risk
CHRISTOPHER SLOBOGIN
VanderbiltUniversityLawSchool
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doi:10.1017/9781108988025
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Contents
Preface:ThePointofThisBook pagevii
1 Rationale
WhatRiskAlgorithmsCanDofortheCriminalJusticeSystem 1
1.1 ThePlagueofMassIncarceration 1
1.2 Why?Crime,Culture,andCodes 7
1.3 ThePotentialRoleofRiskAlgorithms 25
2 Fit
WhyandWhenDataaboutGroupsAreRelevanttoIndividuals 37
2.1 RiskAssessmentInstruments 38
2.2 G2i—UsingInformationaboutGroupstoResolveIndividual
Cases 42
2.3 TheProbabilityCriterion 45
2.4 TheOutcomeCriterion 49
2.5 TheDurationCriterion 52
2.6 TheInterventionCriterion 52
2.7 Conclusion:ImplementingtheFitCriteria 56
2.7.1 PrisonRelease/Diversion 59
2.7.2 PretrialDetentionBasedonRiskofOffending 60
2.7.3 PretrialDetentionBasedonFlightRisk 61
2.7.4 PrisonPopulationReduction 62
3 Validity
FiguringOutWhenRiskAlgorithmsAreSufficientlyAccurate 64
3.1 IncrementalValidityandHelpfulness 65
3.2 CalibrationValidity 68
3.3 DiscriminantValidity 70
v
vi Contents
3.4 ExternalValidity(LocalandGroupValidation) 72
3.5 ImplementationValidity:Inter-RaterReliabilityand
“Adjustments”ofScores 74
3.6 CurrentValidity/Re-Validation 80
3.7 TheMeaningofPredictiveValidity 81
3.8 Conclusion:AssuringAccuracy 82
4. Fairness
AvoidingUnjustAlgorithms 86
4.1 EgalitarianInjustice 86
4.1.1 Race 90
4.1.2 Sex 96
4.1.3 OtherTraits 97
4.2 RetributiveInjustice 99
4.3 ProceduralInjustice 104
4.3.1 Process 105
4.3.2 Transparency 108
4.3.3 Voice 112
4.4 Conclusion:OneCourt’sStrugglewithAssuringFairness 117
5 Structure
LimitingRetributivismandIndividualPrevention 120
5.1 TheProblemswithDesert 124
5.1.1 TheAccuracyofDesert 125
5.1.2 DesertandDisparity 127
5.1.3 DesertandBlameworthiness 129
5.2 PreventiveJusticeandLimitingRetributivism 131
5.2.1 TheDeterminationofSentenceRanges 134
5.2.2 GoverningPrinciplesofPreventiveJustice 138
5.3 ParoleasaConstitutionalRight 142
5.4 PreventiveJusticeandPleaBargaining 147
5.5 Conclusion:PreventiveJusticeinPractice 153
6 MovingForward
TheNeedforExperimentation 158
Index 164
Preface: The Point of This Book
Intoday’sworld,algorithmsareeverywhere.Algorithms—ashorthandwayof
describing statistical and computer driven decision-making processes—are
crucialtoourcommercialand sociallife.Insurancecompaniesusethemto
set premiums, credit card companies rely on them to detect fraud, and
investors depend on them to figure out market trends. Algorithms permeate
the Internet, smartphones,marketing,andtheforecastingofvirtually every-
thing,fromweathertosportsoutcomestopandemics.
Not surprisingly, government agencies rely on algorithms as well, to con-
duct tax audits, welfare checks, police investigations, and DNA and other
forensic analysis.1 Of most significance to this book, for at least a decade
virtually every state has authorized the use of algorithms that purport to
determine the recidivism risk posed by people who have been charged or
convicted of crime.2 Commonly called risk assessment instruments, or RAIs,
thesealgorithmshelpjudgesfigureoutwhetherarrestedindividualsshouldbe
releasedpendingtrialandwhetherconvictedoffendersshouldreceiveprison
time or an enhanced sentence; they assist parole boards in determining
whether to release a prisoner; and they aid correctional officials in deciding
1 SeeDavidFreedmanEngstrometal.,GovernmentbyAlgorithm:ArtificialIntelligencein
FederalAdministrativeAgencies,athttps://ssrn.com/abstract=3551505;SusanMiller,HowLaw
EnforcementUsesForensicAlgorithms,GCN,May12,2020,athttps://gcn.com/articles/2020/
05/12/gao-forensic-algorithms.aspx.
2 Memorandum from the Vera Inst. of Justice, Ctr. on Sentencing & Corr. to Del. Justice
ReinvestmentTaskForce4(Oct.12,2011),athttp://www.ma4jr.org/wp-content/uploads/2014/
10/vera-institute-memo-on-risk-assessment-for-delaware-2011.pdf (“almost every state uses an
assessmenttoolat oneormorepointsinthecriminaljusticesystemto assistinthebetter
managementofoffendersininstitutionsandinthecommunity.Overall,over60community
supervisionagenciesin41statesreportedusinganactuarialassessmenttool,suggestingthatan
overwhelmingmajorityofcorrectionsagenciesnationwideroutinelyutilizeassessmenttoolsto
somedegree.”).
vii
viii Preface
howoffendersshouldbehandledinprison.Mostofthesealgorithmsconsistof
from five to twenty risk factors associated with criminal history, age, and
diagnosis, although an increasing number incorporate other demographic
traits and psychological factors as well. Each of these risk factors correlates
withacertainnumberofpointsthatareusuallyaddedtocomputeaperson’s
riskscore;thehigherthescore,thehighertherisk.Sometoolsmayalsoaimat
identifyingneeds,suchassubstanceabusetreatmentandvocationaltraining,
thoughttoberelevanttorehabilitativeinterventionsthatmightreducerecid-
ivism. This book will provide examples of a number of these instruments so
thatthereadercangetasenseoftheirdiversityandnuances.
Onepurposeofthisbookistoexplainhowriskalgorithmsmightimprove
the criminal justice system. If developed and used properly, RAIs could
become a major tool ofreform. Most importantly, they can helpreduce the
useofpretrialdetentionandprison,aswellasthelengthofprisonsentences,
withoutappreciablyincreasingtheperiltothepublic(goalsthatareparticu-
larlypressingasCOVID-19ravagesourpenalfacilities).Indoingso,theycan
mitigate the excessively punitive bail and sentencing regimes that currently
exist in most states. They can also allocate correctional resources more effi-
cientlyandconsistently.Andtheycanprovidethespringboardforevidence-
basedrehabilitativeprogramsaimedatreducingrecidivism.Morebroadly,by
makingcriminaljusticedecision-makingmoretransparent,thesetoolscould
force long overdue reexamination of the purposes of the criminal justice
systemandoftheoutcomesitshouldbetryingtoachieve.
Despitetheirpotentialadvantages,theriskalgorithmsusedinthecriminal
justicesystemtodayarehighlycontroversial.Acommonclaimisthattheyare
notgoodatwhattheypurporttodo,whichistoidentifywhowilloffendand
whowillnot,whowillberesponsivetorehabilitativeeffortsandwhowillnot
be.Butthetoolsarealsomalignedasraciallybiased,dehumanizing,and,for
goodmeasure,antitheticaltothefoundationalprinciplesofcriminaljustice.
A sampling of recent article and book titles makes the point: “Impoverished
Algorithms: Misguided Governments, Flawed Technologies, and Social
Control,”3“RiskasaProxyforRace:TheDangersofRiskAssessment,”4and
“Automating Inequality: How High-Tech Tools Profile, Police, and Punish
thePoor.”5In2019,over110civilrightsgroupssignedastatementcallingforan
3 SarahValentine,ImpoverishedAlgorithms:MisguidedGovernments,FlawedTechnologies,
andSocialControl,44FordhamUrb.L.364(2019).
4 BernardHarcourt,RiskasaProxyforRace:TheDangersofRiskAssessment,27Fed.Sent.Rep.
237(2015).
5 VirginiaEubanks,AutomatingInequality:HowHigh-TechToolsProfile,Police,andPunish
thePoor(2018).
Preface ix
endtopretrialriskassessmentinstruments.6Thatsameyeartwenty-sevenIvy
LeagueandMITacademicsstatedthat“technicalproblems”withriskassess-
ment instruments “cannot be resolved.”7 And in 2020 another group of 2435
scholarsfromawiderangeofdisciplines“demanded”thatSpringerpublish-
ing company, one of the largest purveyors of healthcare and behavioral
sciencebooksandjournals,“issueastatementcondemningtheuseofcriminal
justicestatisticstopredictcriminality”becauseoftheirunscientificnature.8
Asecondpurposeofthisbookistoexploretheseclaims.Allofthemhave
somebasisinfact.Buttheycaneasilybeoverblown.Andiftheimpactofthese
criticisms is to prevent the criminal justice system from using algorithms,
apotentiallyvaluablemeansofreformwillbelost.Akeyargumentinfavorof
algorithmsiscomparativeinnature.Whilealgorithmscanbeassociatedwith
a number of problems, alternative predictive techniques may well be much
worse in each of these respects. Unstructured decision-making by judges,
parole officers, and mental health professionals is notoriously bad, biased,
and reflexive, and often relies on stereotypes and generalizations that ignore
thegoalsofthesystem.Algorithmscandobetter,atleastifsubjecttocertain
constraints.
Surfacingthoseconstraints is athird purpose of thisbook. Although algo-
rithms, on average, are superior tounstructured judgment when it comes to
prediction, many are seriously defective in a number of respects. This book
provides a set of principles meant to govern the risk assessment enterprise.
Influencedbyinsightsgleanedfromthealgorithmsthemselves,itadvances,in
short,amuch-needed“jurisprudenceofrisk”analogoustothejurisprudence
of criminal liability that has long governed the definition of crimes and the
scopeofpunishment.
Without a jurisprudence of risk, judges and other legal decision-makers
haveverylittleguidanceconcerningwhichRAIs,ifany,areworthyofconsid-
eration, how to evaluate their results, and how much weight to give those
results.Socialscientistsandotherresearcherswhodeveloptheseinstruments
do not have a clear idea of the outcome measures that the law considers
relevant, the types of risk factors they may or may not consider, or the
6 The Use of Pretrial “Risk Assessment” Instruments: A Shared Statement of Civil Rights
Concerns(Jan.2019),athttps://www.supremecourt.ohio.gov/JCS/casemng/PJRSummit/mater
ials/pretrialRiskAssessInstruments.pdf.
7 ChelseaBarabas,KarthikDinakar&ColinDoyle,TheProblemswithRiskAssessmentTools,
N.Y.Times(July17,2019),athttps://dam-prod.media.mit.edu/x/2019/07/16/TechnicalFlawsO
fPretrial_ML%20site.pdf.
8 CoalitionforCriticalTechnology,Abolishthe#TechtoPrisonpipeline(Dec.30,2020),atht
tps://medium.com/@CoalitionForCriticalTechnology/abolish-the-techtoprisonpipeline
-9b5b14366b16.