Evidence-based sentencing and the scientific rationalization of discrimination

Stanford Law Review

Volumn 66, Issue 4, 2014, Pages 803-872

  Starr, Sonja B ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

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[No Author keywords available]

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EID: 84899868948     PISSN: 00389765     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review

References (437)

1. 351 U.S. 12, 16-17 (1956) (plurality opinion) (footnote omitted).
2. Chambers v. Florida, 309 U.S. 227, 241 (1940).
3. At least twelve states have integrated or are currently integrating some form of actuarial risk assessment into sentencing throughout the state pursuant to legislation, state sentencing commission policy, or state supreme court decision. See Presentence Report, Ariz. Jud. Branch, http://www.azcourts.gov/apsd/EvidenceBasedPractice/PresentenceReport.aspx (last visited Mar. 26, 2014) (announcing the inclusion of "evidence based criminogenic factors" in presentence reports in response to an order of the Arizona Supreme Court).
4. See Malenchik v. State, 928 N.E.2d 564, 573-75 (Ind. 2010).
5. Model Penal Code: Sentencing app. A at 133, 135 (Discussion Draft No. 4, 2012).
6. See James Bonta, Offender Risk Assessment and Sentencing, 49 Can. J. Criminology & Crim. Just. 519, 519-20 (2007).
7. See Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age 78-80 (2007) (reviewing state parole practices).
8. See Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age 78-80 (2007) (reviewing state parole practices).
9. E.g., Model Penal Code: Sentencing § 6B.09 cmt. a at 55 (Tentative Draft No. 2, 2011).
10. E.g., Model Penal Code: Sentencing § 6B.09 cmt. a at 55 (Tentative Draft No. 2, 2011).
11. See Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age 78-80 (2007) (reviewing state parole practices).
12. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 1329, 1399-402 (2011) (listing variables in eighteen different instruments).
13. See Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age 78-80 (2007) (reviewing state parole practices).
14. At least twelve states have integrated or are currently integrating some form of actuarial risk assessment into sentencing throughout the state pursuant to legislation, state sentencing commission policy, or state supreme court decision. See Presentence Report, Ariz. Jud. Branch, http://www.azcourts.gov/apsd/EvidenceBasedPractice/PresentenceReport.aspx (last visited Mar. 26, 2014) (announcing the inclusion of "evidence based criminogenic factors" in presentence reports in response to an order of the Arizona Supreme Court).
15. Malenchik v. State, 928 N.E.2d 564, 571-73 (Ind. 2010) (endorsing risk assessment in sentencing).
16. Ky. Rev. Stat. Ann. § 532.007 (West 2013) (requiring all sentencing judges to consider "the results of a defendant's risk and needs assessment included in the presentence investigation").
17. MCOLES-COMPAS at Sentencing-What Every Defense Attorney Needs to Know, Mich. State App. Defender Office, http://www.sado.org/Calendar/Details/146 (last visited Mar. 26, 2014) (announcing trainings in anticipation of Michigan's decision to begin using risk prediction instruments at sentencing on June 1, 2014).
18. Offense Risk Score, Mo. Sent'g Advisory Commission, http://www.mosac.mo.gov/page.jsp?id=45493 (last visited Mar. 26, 2014) (describing Missouri's state-specific instrument).
19. Ohio Rev. Code Ann. § 5120.114 (LexisNexis 2013) (mandating the adoption of a new unified risk assessment tool for use in sentencing as well as for parole and other corrections purposes).
20. Okla. Stat. tit. 22, § 988.19 (2013) (making eligibility for diversion to "community sentences" conditional in most cases on the defendant's score on the Level of Services Inventory (LSI-R).
21. 42 Pa. Cons. Stat. § 2154.7 (2012) (ordering the sentencing commission to develop a risk assessment tool for sentencing use).
22. John Monahan & Jennifer L. Skeem, Risk Redux: The Resurgence of Risk Assessment in Criminal Sanctioning, 26 Fed. Sent'g Rep. 158, 159-60 (2014) (describing Utah sentencing system that requires the incorporation of LSI-R scores into presentence reports and mandates that judges consider them).
23. Va. Criminal Sentencing Comm'n, 2012 Annual Report 6 (2012), available at http://www.vcsc.virginia.gov/ 2012VCSCAnnualReport.pdf (describing program that, since 1994, has used "an empirically-based risk assessment to select 25% of the lowest risk, incarceration-bound, drug and property offenders for placement in alternative (non-prison) sanctions").
24. Wash. Rev. Code § 9.94A.500 (2013) (providing for risk assessment before sentencing if requested by the court and mandating it in all cases involving sex offenses).
25. Press Release, Supreme Court of Appeals, State of W. Va., Court Issues Pre-Sentencing Testing Mandate for All Felons (Jan. 22, 2013), available at http://www.courtswv.gov/public-resources/press/releases/2013-releases/jan22_13.pdf (requiring risk assessments to be provided to sentencing judges in every felony case).
26. See Adult Redeploy Ill. Oversight Bd., 2012 Annual Report to the Governor and General Assembly on the Implementation and Impact of Adult Redeploy Illinois 29-48 (2013), available at http://www.icjia.state.il.us/public/redeploy/pdf/annualreports/2012_adult_redeploy_illinois_ annual_report.pdf (describing various incarceration diversion pilot programs in ten Illinois counties, almost all of which use the LSI-R instrument for assignment purposes).
27. Bernalillo Cnty., New Mexico 2nd Judicial District Criminal Justice Strategic Plan, at ii (2012), available at http://www.bernco.gov/upload/images/commission/dist5/ Bernalillo%20County%20Criminal%20Justice%20Strategic%20Plan.pdf (recommending that Bernalillo County adopt "an automated objective risk assessment tool known as 'COMPAS'" for a variety of purposes, including presentence reports).
28. N.D. Dep't of Corr. & Rehab., Biennial Report for the Period Covering July 1, 2007-June 30, 2009, at 83-84 (2009), available at http://www.nd.gov/docr/media/biennial/archive/BiennialReport%20 2007-09.pdf (describing a pilot program in the Bismarck District that provides "[s]entencing recommendations... based on recidivism risk as measured by the LSI-R"). Finally, in at least five additional states, case law or official reports indicate that risk assessment instruments are, at least in practice, sometimes used in sentencing.
29. See, e.g., State v. Gauthier, 939 A.2d 77, 81, 85-86 (Me. 2007) (upholding a sentence that was based in part on the defendant's high LSI-R risk score).
30. Minn. Dep't of Corr., Study of EvidenceBased Practices in Minnesota: 2011 Report to the Legislature 5 (2010), available at http://archive.leg.state.mn.us/docs/2013/mandated/130241.pdf (reporting that risk assessment tools are "widely used" by supervision agencies in Minnesota "in making sentencing/ disposition recommendations").
31. N.C. Sentencing & Policy Advisory Comm'n, Research Findings and Policy Recommendations from the Correctional Program Evaluations, 2000-2008, at 15 (2009), available at http://www.nccourts.org/Courts/CRS/ Councils/spac/Documents/correctionalevaluation_0209.pdf ("Sentencing Services utilizes [the LSI-R] to develop sentencing plans for approximately 2,000 felony offenders each year at the request of the court or defense counsel.").
32. Brand v. State, 414 S.W.3d 854, 856 (Tex. App. 2013) (noting the inclusion of defendant's LSI-R score in the presentence investigation report).
33. State v. Duchay, 647 N.W.2d 467, 2002 WL 862458, at *1-2 (Wis. Ct. App. May 7, 2002) (unpublished table decision) (rejecting challenge based on presentence investigation report's reliance on the LSI-R score).
34. The most common such instrument is the Static-99. See Static-99/Static-99R, Static99 Clearinghouse, http://www.static99.org (last visited Mar. 26, 2014) ("It is the most widely used sex offender risk assessment instrument in the world, and is extensively used in the United States, Canada, the United Kingdom, Australia, and many European nations.").
35. See Roger K. Warren, Evidence-Based Sentencing: Are We Up To the Task?, 23 Fed. Sent'g Rep. 153, 153 (2010).
36. see also Conference of Chief Justices, Resolution 12 (2007), available at http://www.americanbar.org/content/dam/aba/administrative/legal_ education_and_admissions_to_the_bar/council_reports_and_resolutions/August%202012 %20Council%20Open%20Session%20Materials/2012_ccj_resolution_re_doe_recognition.a uthcheckdam.pdf.
37. Roger K. Warren, Nat'l Ctr. for State Courts, Evidence-Based Practice to Reduce Recidivism: Implications for State Judiciaries (2007), available at http://static.nicic.gov/Library/023358.pdf.
38. 542 U.S. 296 (2004) (holding that Washington State's system of sentencing guidelines violated the Sixth Amendment right to trial by jury).
39. 543 U.S. 220 (2005) (holding that the Federal Sentencing Guidelines violated the Sixth Amendment right to trial by jury).
40. Douglas A. Berman, Are Costs a Unique (and Uniquely Problematic) Kind of Sentencing Data?, 24 Fed. Sent'g Rep. 159, 160 (2012) (emphasis added).
41. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 1399-402 (2011) (listing variables in eighteen different instruments).
42. A few include very basic information such as whether it was a drug crime or a violent crime.
43. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 1399-402 (2011) (listing variables in eighteen different instruments).
44. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 1399-402 (2011) (listing variables in eighteen different instruments).
45. See Model Penal Code: Sentencing § 6B.09 reporter's note at 62 (Tentative Draft No. 2, 2011) (discussing and criticizing this system).
46. Michael H. Marcus, Conversations on Evidence-Based Sentencing, 1 Chap. J. Crim. Just. 61, 107 (2009).
47. See, e.g., Model Penal Code: Sentencing § 6B.09 reporter's note at 62. In 2000, the Supreme Court granted certiorari in a capital case to consider "[w]hether a defendant's race or ethnic background may ever be used as an aggravating circumstance in the punishment phase of a capital murder trial in which the State seeks the death penalty".
48. The issue was not a judicial sentencing instrument but problematic testimony by a prosecution expert. See Saldano v. State, 70 S.W.3d 873, 875 (Tex. Crim. App. 2002) (internal quotation marks omitted) (describing the case's history). Before oral argument, the State of Texas conceded error and granted a new sentencing hearing, mooting the case.
49. See John Monahan, A Jurisprudence of Risk Assessment: Forecasting Harm Among Prisoners, Predators, and Patients, 92 Va. L. Rev. 391, 392-93 (2006).
50. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 1399-402 (2011) (listing variables in eighteen different instruments).
51. Malenchik v. State, 928 N.E.2d 564, 572-74 (Ind. 2010).
52. Dep't of Corrective Servs., LSI-R Training Manual 8 (2002).
53. See Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age 78-80 (2007) (reviewing state parole practices).
54. See Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age 78-80 (2007) (reviewing state parole practices).
55. Malenchik, 928 N.E.2d at 572.
56. The full LSI-R is a proprietary commercial product, but a sample report listing certain risk factors, including "Criminal-Family/Spouse" is publicly available. See Multi-Health Sys. Inc., LSI-R Profile Report for Rex Darlington (2001), available at http://downloads.mhs.com/lsir/lsi-r-5-profile.pdf.
57. COMPAS may be requested from Northpointe Inc. See Northpointe Software Suite, Northpointe, http://www.northpointeinc.com/products/northpointe-software-suite (last visited Mar. 26, 2014).
58. For instance, medical studies suggest that women are on average more vulnerable to addiction and relapse than men are, so it may be that for some drug crimes women are more likely to recidivate. See, e.g., Jill B. Becker & Ming Hu, Sex Differences in Drug Abuse, 29 Frontiers Neuroendocrinology 36, 36 (2008). Recidivism studies typically do not break down gender effects like this, however.
59. The point additions for particular risk factors are at best crudely rounded approximations of regression coefficients. Moreover, the instruments do not track the regression's functional form. The underlying studies typically use logistic regression models, in which the coefficients translate nonlinearly into changes in probability of recidivism. When the instruments translate the coefficients into fixed, additive increases on a point scale, they are "linearizing" the variables' effects, and the resulting instrument will be only loosely related to the underlying nonlinear model, especially (because of the probability curve's shape) for very high-risk or very low-risk cases.
60. Michael A. Wolff, Missouri's Information-Based Discretionary Sentencing System, 4 Ohio St. J. Crim. L. 95, 113 (2006).
61. Michael A. Wolff, Missouri's Information-Based Discretionary Sentencing System, 4 Ohio St. J. Crim. L. 112-13 (2006).
62. Michael A. Wolff, Missouri's Information-Based Discretionary Sentencing System, 4 Ohio St. J. Crim. L. 112-13 (2006).
63. Michael A. Wolff, Missouri's Information-Based Discretionary Sentencing System, 4 Ohio St. J. Crim. L. 112-13 (2006).
64. Jordan M. Hyatt et al., Follow the Evidence: Integrate Risk Assessment into Sentencing, 23 Fed. Sent'g Rep. 266, 266 (2011).
65. see, e.g., Lynn S. Branham, Follow the Leader: The Advisability and Propriety of Considering Cost and Recidivism Data at Sentencing, 24 Fed. Sent'g Rep. 169, 169 (2012).
66. Richard E. Redding, Evidence-Based Sentencing: The Science of Sentencing Policy and Practice, 1 Chap. J. Crim. Just. 1, 1 & n.4, 5-6 (2009) (reviewing articles praising EBS and stating that failure to employ EBS "constitutes sentencing malpractice and professional incompetence").
67. See, e.g., Richard P. Kern & Mark H. Bergstrom, A View from the Field: Practitioners' Response to Actuarial Sentencing; An "Unsettled" Proposition, 25 Fed. Sent'g Rep. 185 (2013) (containing a promotion of EBS written by directors of the Virginia Criminal Sentencing Commission and Pennsylvania Commission on Sentencing).
68. Michael H. Marcus, Conversations on Evidence-Based Sentencing, 1 Chap. J. Crim. Just. 61, 107 (2009).
69. Roger K. Warren, Nat'l Ctr. for State Courts, Evidence-Based Practice to Reduce Recidivism: Implications for State Judiciaries (2007), available at http://static.nicic.gov/Library/023358.pdf.
70. Michael A. Wolff, Evidence-Based Judicial Discretion: Promoting Public Safety Through State Sentencing Reform, 83 N.Y.U. L. Rev. 1389 (2008) (containing a promotion of EBS written by the chair of the Missouri Sentencing Advisory Commission).
71. William Ray Price, Jr., Chief Justice, Supreme Court of Mo., State of the Judiciary Address (Feb. 3, 2010) (transcript available at http://www.courts.mo.gov/page.jsp?id=36875).
72. See, e.g., Pamela M. Casey et al., Nat'l Ctr. for State Courts, Using Offender Risk and Needs Assessment Information at Sentencing: Guidance for Courts from a National Working Group (2011), available at http://www.ncsc.org/ ~/media/Files/PDF/Services%20and%20Experts/Areas%20of%20expertise/Sentencing%20P robation/RNA%20Guide%20Final.ashx.
73. Nat'l Ctr. for State Courts, Evidence-Based Sentencing to Improve Public Safety & Reduce Recidivism: A Model Curriculum for Judges (2009), available at http://www.ncsc.org/sitecore/content/microsites/csi/home/~/ media/Microsites/Files/CSI/Education/Faculty_Handbook.ashx.
74. Roger K. Warren, Pew Ctr. on the States, Public Safety Policy Brief No. 8, Arming the Courts with Research: 10 Evidence-Based Sentencing Initiatives to Control Crime and Reduce Costs 1-3 (2009), available at http://www.colorado.gov/ccjjdir/Resources/Resources/ Ref/PEW_ArmingTheCourtWithResearch.pdf.
75. Matthew Kleiman, Using Evidence-Based Practices in Sentencing Criminal Offenders, in 44 The Book of the States 299 (Council of State Gov'ts ed., 2012), available at http://knowledgecenter.csg.org/drupal/system/ files/matthew_kleiman_2012.pdf.
76. See, e.g., Pamela M. Casey et al., Nat'l Ctr. for State Courts, Using Offender Risk and Needs Assessment Information at Sentencing: Guidance for Courts from a National Working Group (2011), available at http://www.ncsc.org/ ~/media/Files/PDF/Services%20and%20Experts/Areas%20of%20expertise/Sentencing%20P robation/RNA%20Guide%20Final.ashx.
77. See, e.g., Margareth Etienne, Legal and Practical Implications of Evidence-Based Sentencing by Judges, 1 Chap. J. Crim. Just. 43 (2009).
78. Gerard E. Lynch, Revising the Model Penal Code: Keeping It Real, 1 Ohio St. J. Crim. L. 219, 220 (2003) (observing, in addition, that the MPC's classroom use makes it "the document through which most American lawyers come to understand criminal law").
79. Model Penal Code: Sentencing § 6B.09(2) (Tentative Draft No. 2, 2011).
80. Model Penal Code: Sentencing § 6B.09(2) (Tentative Draft No. 2, 2011).
81. See Model Penal Code: Sentencing app. A at 133, 135 (Discussion Draft No. 4, 2012).
82. Model Penal Code: Sentencing § 6B.09 cmt. a at 53, 55 (Tentative Draft No. 2, 2011).
83. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 1399-402 (2011) (listing variables in eighteen different instruments).
84. Michael A. Wolff, Evidence-Based Judicial Discretion: Promoting Public Safety Through State Sentencing Reform, 83 N.Y.U. L. Rev. 1389 (2008) (containing a promotion of EBS written by the chair of the Missouri Sentencing Advisory Commission).
85. Stephen D. Gottfredson & Laura J. Moriarty, Clinical Versus Actuarial Judgments in Criminal Justice Decisions: Should One Replace the Other?, Fed. Probation, Sept. 2006, at 15, 15 ("In virtually all decision-making situations that have been studied, actuarially developed devices outperform human judgments. ").
86. Patricia M. Harris, What Community Supervision Officers Need to Know About Actuarial Risk Assessment and Clinical Judgment, Fed. Probation, Sept. 2006, at 8, 9-11 (summarizing literature supporting superiority of actuarial approaches).
87. See, e.g., Pamela M. Casey et al., Nat'l Ctr. for State Courts, Using Offender Risk and Needs Assessment Information at Sentencing: Guidance for Courts from a National Working Group (2011), available at http://www.ncsc.org/ ~/media/Files/PDF/Services%20and%20Experts/Areas%20of%20expertise/Sentencing%20P robation/RNA%20Guide%20Final.ashx.
88. Roger K. Warren, Nat'l Ctr. for State Courts, Evidence-Based Practice to Reduce Recidivism: Implications for State Judiciaries (2007), available at http://static.nicic.gov/Library/023358.pdf.
89. Michael Marcus, MPC-The Root of the Problem: Just Deserts and Risk Assessment, 61 Fla. L. Rev. 751, 751 (2009).
90. Michael A. Wolff, Evidence-Based Judicial Discretion: Promoting Public Safety Through State Sentencing Reform, 83 N.Y.U. L. Rev. 1389 (2008) (containing a promotion of EBS written by the chair of the Missouri Sentencing Advisory Commission).
91. William Ray Price, Jr., Chief Justice, Supreme Court of Mo., State of the Judiciary Address (Feb. 3, 2010) (transcript available at http://www.courts.mo.gov/page.jsp?id=36875).
92. See James Bonta, Offender Risk Assessment and Sentencing, 49 Can. J. Criminology & Crim. Just. 524 (2007).
93. See, e.g., Margareth Etienne, Legal and Practical Implications of Evidence-Based Sentencing by Judges, 1 Chap. J. Crim. Just. 43 (2009).
94. Model Penal Code: Sentencing § 6B.09 cmt. a at 54 (Tentative Draft No. 2, 2011).
95. Model Penal Code: Sentencing § 6B.09 cmt. a at 54 (Tentative Draft No. 2, 2011).
96. Model Penal Code: Sentencing § 6B.09 cmt. a at 54 (Tentative Draft No. 2, 2011).
97. Model Penal Code: Sentencing § 6B.09 cmt. a at 54 (Tentative Draft No. 2, 2011).
98. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 1399-402 (2011) (listing variables in eighteen different instruments).
99. See Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age 78-80 (2007) (reviewing state parole practices).
100. See Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age 78-80 (2007) (reviewing state parole practices).
101. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 1399-402 (2011) (listing variables in eighteen different instruments).
102. See John Monahan, A Jurisprudence of Risk Assessment: Forecasting Harm Among Prisoners, Predators, and Patients, 92 Va. L. Rev. 429 (2006).
103. E.g., Alexander Scherr, Daubert & Danger: The "Fit" of Expert Predictions in Civil Commitments, 55 Hastings L.J. 1, 5-28 (2003) (reviewing case law and literature). I do not focus on the evidence law issues here.
104. See John Monahan, A Jurisprudence of Risk Assessment: Forecasting Harm Among Prisoners, Predators, and Patients, 92 Va. L. Rev. 427-28 (2006).
105. Model Penal Code: Sentencing § 6B.09 cmt. e at 56-57 (Tentative Draft No. 2, 2011).
106. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 1399-402 (2011) (listing variables in eighteen different instruments).
107. Peter Moskos, Book Review,113 Am. J. Soc. 1475 (2008).
108. See Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age 78-80 (2007) (reviewing state parole practices).
109. See Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age 78-80 (2007) (reviewing state parole practices).
110. Yoav Sapir, Against Prevention? A Response to Harcourt's Against Prediction on Actuarial and Clinical Predictions and the Faults of Incapacitation, 33 Law & Soc. Inquiry 253, 258-62 (2008) (arguing that the problem with the instruments is really a broader problem with incapacitation as a punishment objective, including its pursuit via clinical judgment).
111. See, e.g., Paul H. Robinson, Commentary, Punishing Dangerousness: Cloaking Preventive Detention as Criminal Justice, 114 Harv. L. Rev. 1429, 1429-32 (2001).
112. See, e.g., Graham v. Florida, 560 U.S. 48, 76-77 (2010) (assuming the mitigating role of young age).
113. See Barefoot v. Estelle, 463 U.S. 880, 896-99 (1983).
114. see also Jurek v. Texas, 428 U.S. 262, 275 (1976) (plurality opinion) ("[P]rediction of future criminal conduct is an essential element in many of the decisions rendered throughout our criminal justice system. ").
115. See Model Penal Code: Sentencing § 6B.09 reporter's note at 62 (Tentative Draft No. 2, 2011).
116. Bernard E. Harcourt, Risk as a Proxy for Race, Criminology & Pub. Pol'y (forthcoming) (manuscript at 2), available at http://papers.ssrn.com/sol3/papers.cfm? abstract_id=1677654.
117. Bernard E. Harcourt, Risk as a Proxy for Race, Criminology & Pub. Pol'y (forthcoming) (manuscript at 2), available at http://papers.ssrn.com/sol3/papers.cfm? abstract_id=1677654.
118. Kelly Hannah-Moffat, Actuarial Sentencing: An "Unsettled" Proposition, 30 Just. Q. 270, 279-84 (2013).
119. See, e.g., Pari McGarraugh, Note, Up or Out: Why "Sufficiently Reliable" Statistical Risk Assessment Is Appropriate at Sentencing and Inappropriate at Parole, 97 Minn. L. Rev. 1079, 1101-02 (2013) (briefly asserting that gender as well as race "must be purged from the list of inputs" but providing little explanation).
120. Christopher Slobogin, Risk Assessment and Risk Management in Juvenile Justice, Crim. Just., Winter 2013, at 10, 13-15 (very briefly addressing possible equal protection objections and concluding that while use of race, ethnicity, or religion is probably impermissible, gender and age are "less open to constitutional challenge").
121. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 1399-402 (2011) (listing variables in eighteen different instruments).
122. Christopher Slobogin, Risk Assessment and Risk Management in Juvenile Justice, Crim. Just., Winter 2013, at 13-14 (very briefly addressing possible equal protection objections and concluding that while use of race, ethnicity, or religion is probably impermissible, gender and age are "less open to constitutional challenge").
123. See John Monahan, A Jurisprudence of Risk Assessment: Forecasting Harm Among Prisoners, Predators, and Patients, 92 Va. L. Rev. 427-28 (2006).
124. See, e.g., Pari McGarraugh, Note, Up or Out: Why "Sufficiently Reliable" Statistical Risk Assessment Is Appropriate at Sentencing and Inappropriate at Parole, 97 Minn. L. Rev. 1079, 1101-02 (2013) (briefly asserting that gender as well as race "must be purged from the list of inputs" but providing little explanation).
125. Christopher Slobogin, Risk Assessment and Risk Management in Juvenile Justice, Crim. Just., Winter 2013, at 16-17 (very briefly addressing possible equal protection objections and concluding that while use of race, ethnicity, or religion is probably impermissible, gender and age are "less open to constitutional challenge").
126. Kelly Hannah-Moffat, Actuarial Sentencing: An "Unsettled" Proposition, 30 Just. Q. 270, 279-84 (2013).
127. Model Penal Code: Sentencing § 6B.09 cmt. e at 56-57 (Tentative Draft No. 2, 2011).
128. E.g., Stephen J. Morse, Mental Disorder and Criminal Law, 101 J. Crim. L. & Criminology 885, 944 (2011).
129. Christopher Slobogin, Risk Assessment and Risk Management in Juvenile Justice, Crim. Just., Winter 2013, at 15 (very briefly addressing possible equal protection objections and concluding that while use of race, ethnicity, or religion is probably impermissible, gender and age are "less open to constitutional challenge").
130. see also Barefoot v. Estelle, 463 U.S. 880, 896-99 (1983).
131. In Barefoot, the Court made clear that the defects in evidence would have to be extreme before their admission would be barred by the Due Process Clause on the grounds of sheer unreliability. 463 U.S. at 898-99.
132. See United States v. Sprei, 145 F.3d 528, 535 (2d Cir. 1998) (describing stigma and reduced marital prospects as an "inevitable result" of a parent's incarceration).
133. Miriam J. Aukerman, The Somewhat Suspect Class: Towards a Constitutional Framework for Evaluating Occupational Restrictions Affecting People with Criminal Records, 7 J.L. Soc'y 18, 51 (2005) (reviewing case law and identifying factors that often trigger heightened scrutiny of a classification, including the use of immutable characteristics, associated social disadvantage, and irrelevance to legitimate state objectives).
134. John Hagan & Ronit Dinovitzer, Collateral Consequences of Imprisonment for Children, Communities, and Prisoners, 26 Crime & Just. 121 (1999) (reviewing literature on effects of parental incarceration).
135. Sandra Stukes Chipungu & Tricia B. Bent-Goodley, Meeting the Challenges of Contemporary Foster Care, Future Child., Winter 2004, at 75, 85 (describing stigma and educational and social disadvantages experienced by children in foster care).
136. see also Mathews v. Lucas, 427 U.S. 495 (1976) (establishing illegitimacy as a quasi-suspect classification).
137. 500 U.S. 453, 464-65 (1991). In Chapman, the defendant challenged the Federal Sentencing Guidelines' method of calculating LSD weight, which included the carrier medium.
138. 500 U.S. 453, 464-65 (1991). In Chapman, the defendant challenged the Federal Sentencing Guidelines' method of calculating LSD weight, which included the carrier medium.
139. Carissa Byrne Hessick & F. Andrew Hessick, Recognizing Constitutional Rights at Sentencing, 99 Calif. L. Rev. 47, 49 (2011), and it presumably worried that doing so in that case would require the extension of strict scrutiny to virtually every sentencing distinction. However, the Court's reasoning fails to take seriously the tremendous stakes of sentencing choices within statutory ranges. Those ranges are often very broad (say, 0 to 20 years), and it is hard to imagine any government decision that more drastically impacts a defendant's exercise of fundamental liberties than the choice between, say, five and twenty years of incarceration. Moreover, the Court's characterization of the right at issue was unduly narrow.
140. the question is not whether the defendant had a right to a sentence below the statutory maximum. Rather, the sentencing decision directly interferes with underlying fundamental rights (including the defendant's most basic physical liberty). Cf. Lawrence v. Texas, 539 U.S. 558, 567 (2003) (critiquing the Court's past, overly narrow characterization of the right to sexual intimacy as a "right to engage in consensual sodomy"). The outcome in Chapman is perfectly defensible, but it could have been reached with a different rationale. The drug-weighing rule was a classification of criminal conduct, not persons, and thus (absent evidence of discriminatory motive) raised no equal protection concerns at all.
141. All persons are prospectively subject to the same weighing rules. Applying fundamental rights analysis to EBS thus would not require routine sentencing distinctions between crimes to be subject to strict scrutiny. One could likewise defend sentencing distinctions based on criminal history as conduct based and universal-all who commit crimes subject themselves to higher sentences for subsequent crimes. But when the state systematically gives different sentences to different groups of people, with no distinctions in current or past criminal conduct, the Constitution should demand a compelling justification.
142. See John Monahan, A Jurisprudence of Risk Assessment: Forecasting Harm Among Prisoners, Predators, and Patients, 92 Va. L. Rev. 427-28 (2006).
143. see also Edward Latessa et al., Creation and Validation of the Ohio Risk Assessment System: Final Report 26 (2009), available at http://www.uc.edu/content/dam/uc/ccjr/docs/reports/project_reports/ORAS_Final_ Report.pdf (describing Ohio's statewide system, which uses different risk category cutoffs for men and women, though its various risk scales vary in terms of which cutoff is higher).
144. Brian J. Ostrom et al., Nat'l Ctr. for State Courts, Offender Risk Assessment in Virginia 5, 123 (2002), available at http://www.vcsc.virginia.gov/risk_off_rpt.pdf (describing the role of gender in Virginia's instrument and acknowledging that young male offenders have a very difficult time qualifying for diversion from incarceration).
145. Wash. State Dep't of Corr., Static Risk Assessment (2007), available at http://www.ofm.wa.gov/ sgc/meetings/2008/06/SGCmeeting_20080613_StaticRiskAssessment.pdf (describing factors in Washington's instrument). The COMPAS software suite includes a completely separate version designed for female offenders.
146. See Women, Northpointe, http://www.northpointeinc.com/solutions/women (last visited Mar. 26, 2014).
147. United States v. Virginia, 518 U.S. 515, 531 (1996) (internal quotation marks omitted).
148. United States v. Virginia, 518 U.S. 515, 531 (1996) (internal quotation marks omitted).
149. Christopher Slobogin, Risk Assessment and Risk Management in Juvenile Justice, Crim. Just., Winter 2013, at 10, 13-15 (very briefly addressing possible equal protection objections and concluding that while use of race, ethnicity, or religion is probably impermissible, gender and age are "less open to constitutional challenge").
150. See Model Penal Code: Sentencing § 6B.09 reporter's note at 62 (Tentative Draft No. 2, 2011).
151. See Model Penal Code: Sentencing § 6B.09 reporter's note at 62 (Tentative Draft No. 2, 2011).
152. See, e.g., United States v. Maples, 501 F.2d 985, 987 (4th Cir. 1974).
153. Williams v. Currie, 103 F. Supp. 2d 858, 868 (M.D.N.C. 2000).
154. Carissa Byrne Hessick, Race and Gender as Explicit Sentencing Factors, 14 J. Gender Race & Just. 127, 137 & n.68 (2010).
155. See, e.g., Sonja B. Starr, Estimating Gender Disparities in Federal Criminal Cases 3-4, 17 (Univ. of Mich. Law Sch. Law & Econ. Research Paper Series, Paper No. 12-018, 2012), available at http://ssrn.com/abstract=2144002 (finding large gender gaps at multiple procedural stages that are unexplained by observable variables and also reviewing other studies).
156. Carissa Byrne Hessick, Race and Gender as Explicit Sentencing Factors, 14 J. Gender Race & Just. 127, 137 & n.68 (2010).
157. Carissa Byrne Hessick, Race and Gender as Explicit Sentencing Factors, 14 J. Gender Race & Just. 127, 137 & n.68 (2010).
158. U.S. Sentencing Guidelines Manual § 5H1.10 (2012).
159. See, e.g., Oren Gazal-Ayal, Foreword: A Global Perspective on Sentencing Reforms, 76 Law & Contemp. Probs., at i, iii-iv (2013).
160. Mona Lynch, Expanding the Empirical Picture of Federal Sentencing: An Invitation, 23 Fed. Sent'g Rep. 313, 314 (2011). Some scholars criticize increasing female incarceration rates, but they do not generally argue that women should receive lower sentences based on gender per se. Rather, they argue that the system should take more account of certain mitigating factors that are more often present in female defendants' cases.
161. Leslie Acoca & Myrna S. Raeder, Severing Family Ties: The Plight of Nonviolent Female Offenders and Their Children, 11 Stan. L. & Pol'y Rev. 133, 135, 141 (1999).
162. Phyllis Goldfarb, Counting the Drug War's Female Casualties, 6 J. Gender Race & Just. 277, 291-93 (2002).
163. See John Monahan, A Jurisprudence of Risk Assessment: Forecasting Harm Among Prisoners, Predators, and Patients, 92 Va. L. Rev. 431 (2006).
164. See Margareth Etienne, Sentencing Women: Reassessing the Claims of Disparity, 14 J. Gender Race & Just. 73, 82 (2010).
165. Michael H. Marcus, Conversations on Evidence-Based Sentencing, 1 Chap. J. Crim. Just. 61, 107 (2009).
166. See Kristy Holtfreter & Rhonda Cupp, Gender and Risk Assessment: The Empirical Status of the LSI-R for Women, 23 J. Contemp. Crim. Just. 363, 363 (2007).
167. But see Paula Smith et al., Can 14,737 Women Be Wrong? A Meta-Analysis of the LSI-R and Recidivism for Female Offenders, 8 Criminology & Pub. Pol'y 183, 183 (2009) (finding that the LSI-R is just as accurate for women as for men).
168. This is a concept that has traditionally (although subject to some limitations) dominated insurance law.
169. See, e.g., Tom Baker, Health Insurance, Risk, and Responsibility After the Patient Protection and Affordable Care Act, 159 U. Pa. L. Rev. 1577, 1597-600 (2011).
170. See 518 U.S. 515, 533-34 (1996).
171. 429 U.S. 190, 191-92 (1976).
172. 429 U.S. 190, 191-92 (1976).
173. 429 U.S. 202 (1976).
174. J.E.B. v. Alabama ex rel. T.B., 511 U.S. 127, 135 (1994).
175. Virginia, 518 U.S. at 533.
176. See John Monahan, A Jurisprudence of Risk Assessment: Forecasting Harm Among Prisoners, Predators, and Patients, 92 Va. L. Rev. 427-28 (2006).
177. See Weinberger v. Wiesenfeld, 420 U.S. 636, 645 (1975).
178. Frontiero v. Richardson, 411 U.S. 677, 690-91 (1973).
179. Wiesenfeld, 420 U.S. at 645.
180. 511 U.S. at 148-49 (O'Connor, J., concurring).
181. 511 U.S. at 148-49 (O'Connor, J., concurring).
182. 511 U.S. at 148-49 (O'Connor, J., concurring).
183. 518 U.S. 515, 541 (1996) (internal quotation marks omitted).
184. 518 U.S. 515, 541 (1996) (internal quotation marks omitted).
185. 518 U.S. 515, 541 (1996) (internal quotation marks omitted).
186. see Metro Broad., Inc. v. FCC, 497 U.S. 547, 620 (1990) (O'Connor, J., dissenting) (citing this passage to inform the application of the Equal Protection Clause).
187. See Richard A. Primus, Equal Protection and Disparate Impact: Round Three, 117 Harv. L. Rev. 493, 553 (2003).
188. See Jack M. Balkin & Reva B. Siegel, The American Civil Rights Tradition: Anticlassification or Antisubordination?, 58 U. Miami L. Rev. 9, 9-10 (2003) (reviewing antisubordinationist scholarship).
189. See Richard A. Primus, Equal Protection and Disparate Impact: Round Three, 117 Harv. L. Rev. 493, 553 (2003).
190. See Samuel R. Bagenstos, "Rational Discrimination, " Accommodation, and the Politics of (Disability) Civil Rights, 89 Va. L. Rev. 825, 855-59 (2003).
191. David A. Strauss, The Myth of Colorblindness, 1986 Sup. Ct. Rev. 99, 109-11.
192. David A. Strauss, The Myth of Colorblindness, 1986 Sup. Ct. Rev. 110.
193. Model Penal Code: Sentencing § 6B.09 cmt. e at 56-57 (Tentative Draft No. 2, 2011).
194. 518 U.S. 515, 533 (1996).
195. 533 U.S. 53, 73 (2001).
196. 533 U.S. 53, 73 (2001).
197. 533 U.S. 53, 73 (2001).
198. See Frederick Schauer, Profiles, Probabilities, and Stereotypes (2003).
199. See Frederick Schauer, Profiles, Probabilities, and Stereotypes (2003).
200. The problem is not that the instruments generalize, but that they employ particular kinds of generalizations that are insidious, and constitutionally suspect, in a context that has huge consequences for individuals and communities.
201. See Nguyen, 533 U.S. at 76 (O'Connor, J., dissenting).
202. see also Virginia, 518 U.S. at 533.
203. See, e.g., Maher v. Roe, 432 U.S. 464, 471 (1977) ("[T]his Court has never held that financial need alone identifies a suspect class for purposes of equal protection analysis. ").
204. 351 U.S. 12, 17 (1956) (plurality opinion).
205. 351 U.S. 12, 17 (1956) (plurality opinion).
206. accord Mayer v. City of Chi., 404 U.S. 189, 193-97 (1971).
207. Ake v. Oklahoma, 470 U.S. 68, 76 (1985).
208. see also Gideon v. Wainwright, 372 U.S. 335, 344 (1963) (citing the goal of achieving a justice system in which, regardless of finances, "every defendant stands equal before the law").
209. Lewis v. Casey, 518 U.S. 343, 369-70, 371 & n.2, 372 (1996).
210. Lewis v. Casey, 518 U.S. 372 (1996).
211. see also Douglas v. California, 372 U.S. 353, 355 (1963).
212. 461 U.S. 660, 663 (1983).
213. 461 U.S. 660, 663 (1983).
214. 461 U.S. at 671 (footnote omitted).
215. 461 U.S. at 671 (footnote omitted).
216. 461 U.S. at 671 (footnote omitted).
217. 461 U.S. at 671 (footnote omitted).
218. See also Williams, 399 U.S. at 244 (stating that ability to pay can be considered to avoid "inverse discrimination").
219. United States v. Altamirano, 11 F.3d 52, 53 (5th Cir. 1993) (discussing the circumstances in which courts can consider indigency).
220. See, e.g., Powers v. Hamilton Cnty. Pub. Defender Comm'n, 501 F.3d 592, 608 (6th Cir. 2007)
221. Alkire v. Irving, 330 F.3d 802, 816-17 (6th Cir. 2003).
222. Bearden, 461 U.S. at 671-72. Similarly, Justice White wrote that because "[p]overty does not insulate those who break the law from punishment, " the poor may be imprisoned if they cannot pay fines, but only "if the sentencing court makes a good-faith effort to impose a jail sentence that in terms of the State's sentencing objectives will be roughly equivalent to the fine and restitution that the defendant failed to pay. "
223. Bearden, 461 U.S. at 671-72. Similarly, Justice White wrote that because "[p]overty does not insulate those who break the law from punishment, " the poor may be imprisoned if they cannot pay fines, but only "if the sentencing court makes a good-faith effort to impose a jail sentence that in terms of the State's sentencing objectives will be roughly equivalent to the fine and restitution that the defendant failed to pay. "
224. Williams, 399 U.S. at 260 (Harlan, J., concurring in the result).
225. see Evitts v. Lucey, 469 U.S. 387, 405 (1985) (discussing the interrelationship between due process and equal protection concerns in these cases).
226. United States v. Burgum, 633 F.3d 810, 816 (9th Cir. 2011).
227. accord United States v. Parks, 89 F.3d 570, 572 (9th Cir. 1996) ("[The defendant] may be receiving an additional eight months on this sentence due to poverty. Such a result is surely anathema to the Constitution. ").
228. see also United States v. Ellis, 907 F.2d 12, 13 (1st Cir. 1990) ("[T]he government cannot keep a person in prison solely because of indigency. ").
229. But see State v. Todd, 208 P.3d 303, 305-06 (Idaho Ct. App. 2009) (upholding inability to pay as an aggravating factor).
230. 552 U.S. 38 (2007).
231. United States v. Bragg, 582 F.3d 965, 970 (9th Cir. 2009).
232. See, e.g., Johnson v. Prast, 548 F.2d 699, 703 (7th Cir. 1977).
233. King v. Wyrick, 516 F.2d 321, 323 (8th Cir. 1975).
234. Ham v. North Carolina, 471 F.2d 406, 408 (4th Cir. 1973).
235. Vasquez v. Cooper, 862 F.2d 250, 251-52 (10th Cir. 1988) (finding no constitutional violation in the court's refusal to credit the defendant for time he served in custody prior to trial because he could not make bail).
236. United States v. Luster, 889 F.2d 1523, 1530 (6th Cir. 1989).
237. see also Kadrmas v. Dickinson Pub. Sch., 487 U.S. 450, 461 & n. * (1988) (rejecting heightened scrutiny in a noncriminal case because "the criminal-sentencing decision at issue in Bearden is not analogous to the user fee... before us").
238. Maher v. Roe, 432 U.S. 464, 471 n.6 (1977).
239. Dickerson v. Latessa, 872 F.2d 1116, 1119-20 (1st Cir. 1989) (observing that classifications implicating appeal rights receive heightened scrutiny only if they are wealth based).
240. United States v. Avendano-Camacho, 786 F.2d 1392, 1394 (9th Cir. 1986) ("At least where the classification at issue is not based on wealth, the right to appeal is not a fundamental right. ").
241. United States v. Kerr, 686 F. Supp. 1174, 1178 (W.D. Pa. 1988).
242. 686 F. Supp. at 1178.
243. Williams v. Illinois, 399 U.S. 235, 263 (1970) (Harlan, J., concurring in the result).
244. U.S. Sentencing Guidelines Manual § 5H1.10 (2012).
245. see also Joan Petersilia & Susan Turner, Guideline-Based Justice: Prediction and Racial Minorities, in 9 Crime and Justice: A Review of Research 151, 153-54, 160 (Don M. Gottfredson & Michael Tonry eds., 1987) (describing sentencing reformers' objective of eliminating role of "status factors" such as employment).
246. E.g., United States v. Trimble, 514 F. App'x 913, 915 (11th Cir. 2013).
247. U.S. Sentencing Guidelines Manual § 4B1.3.
248. See United States v. Rivera, 694 F. Supp. 1105, 1106-07 (S.D.N.Y. 1988).
249. see also United States v. Luster, 889 F.2d 1523, 1528-29 (6th Cir. 1989) (describing the amendment). The current quantitative inquiry concerns only the amount of criminal income.
250. There is also a qualitative inquiry into whether crime was the defendant's "primary occupation. " U.S. Sentencing Guidelines Manual § 4B1.3 cmt. application notes (italics omitted).
251. Luster, 889 F.2d at 1529-30.
252. United States v. Kerr, 686 F. Supp. 1174, 1179 (W.D. Pa. 1988).
253. Papachristou v. City of Jacksonville, 405 U.S. 156, 171 (1972).
254. cf. Edwards v. California, 314 U.S. 160, 177 (1941) ("[W]e do not think that it will now be seriously contended that because a person is without employment and without funds he constitutes a 'moral pestilence.' Poverty and immorality are not synonymous. ").
255. 461 U.S. 660, 671 (1983).
256. Brief for Respondent at 32-35.
257. Bearden, 461 U.S. 660 (No. 81-6633), 1982 U.S. S. Ct. Briefs LEXIS 438, at *35-39.
258. Given fairly high levels of residential segregation, see generally John Iceland et al., U.S. Census Bureau, Series CENSR-3, Racial and Ethnic Residential Segregation in the United States: 1980-2000 (2002), available at http://www.census.gov/hhes/www/housing/housing_patterns/pdf/censr-3.pdf, neighborhood might also be a racial proxy, but challengers would likely have trouble proving a racially discriminatory purpose.
259. Given fairly high levels of residential segregation, see generally John Iceland et al., U.S. Census Bureau, Series CENSR-3, Racial and Ethnic Residential Segregation in the United States: 1980-2000 (2002), available at http://www.census.gov/hhes/www/housing/housing_patterns/pdf/censr-3.pdf, neighborhood might also be a racial proxy, but challengers would likely have trouble proving a racially discriminatory purpose.
260. See Heather C. West, Bureau of Justice Statistics, U.S. Dep't of Justice, Prison Inmates at Midyear 2009-Statistical Tables 21 tbl.18 (2010), available at http://www.bjs.gov/content/pub/pdf/pim09st.pdf (estimating that 4749 black men per 100,000 were in custody in 2009 as compared with 91 white women per 100,000).
261. Jenifer Warren, Pew Ctr. on the States, One in 100: Behind Bars in America 2008, at 3 (2008), available at http://www.pewstates.org/uploadedFiles/PCS_Assets/ 2008/one%20in%20100.pdf.
262. Thomas P. Bonczar, Bureau of Justice Statistics, U.S. Dep't of Justice, Prevalence of Imprisonment in the U.S. Population, 1974-2001, at 1 (2003), available at http://www.bjs.gov/content/pub/pdf/piusp01.pdf.
263. Andrew Sum et al., The Consequences of Dropping Out of High School: Joblessness and Jailing for High School Dropouts and the High Cost for Taxpayers 10 (Ctr. for Labor Mkt. Studies Publ'ns, Paper No. 23, 2009), available at http://hdl.handle.net/ 2047/d20000596.
264. see also Robert J. Sampson & Charles Loeffler, Punishment's Place: The Local Concentration of Mass Incarceration, Daedalus, Summer 2010, at 20, 20 (discussing neighborhood effects).
265. See generally Michelle Alexander, The New Jim Crow: Mass Incarceration in the Age of Colorblindness (rev. ed. 2012).
266. Todd R. Clear, Imprisoning Communities: How Mass Incarceration Makes Disadvantaged Neighborhoods Worse (2007).
267. Imprisoning America: The Social Effects of Mass Incarceration (Mary Pattillo et al. eds., 2004).
268. See U.S. Census Bureau, Statistical Abstract of the United States: 2012, at 151 tbl.229 (2012), available at http://www.census.gov/compendia/statab/2012/tables/ 12s0229.pdf (showing that blacks and particularly Hispanics have lower high school and college graduation rates than whites).
269. See Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age 78-80 (2007) (reviewing state parole practices).
270. Table A-2: Employment Status of the Civilian Population by Race, Sex, and Age, Bureau Lab. Stat. (Sept. 6, 2013), http://www.bls.gov/ news.release/empsit.t02.htm (showing unemployment rates approximately twice as high for blacks than for whites).
271. See Kyle Crowder et al., Neighborhood Diversity, Metropolitan Constraints, and Household Migration, 77 Am. Soc. Rev. 325, 325-26 (2012) (discussing continued "moderate to high" levels of black-white neighborhood segregation in large metropolitan areas and "steady or increased" segregation of Asian and Latino populations from blacks and whites).
272. John Monahan & Jennifer L. Skeem, Risk Redux: The Resurgence of Risk Assessment in Criminal Sanctioning, 26 Fed. Sent'g Rep. 158, 159-60 (2014) (describing Utah sentencing system that requires the incorporation of LSI-R scores into presentence reports and mandates that judges consider them).
273. See, e.g., Washington v. Davis, 426 U.S. 229, 238-39 (1976).
274. Todd R. Clear, Imprisoning Communities: How Mass Incarceration Makes Disadvantaged Neighborhoods Worse (2007).
275. William A. Darity, Jr. & Samuel L. Myers, Jr., Family Structure and the Marginalization of Black Men: Policy Implications, in The Decline in Marriage Among African Americans: Causes, Consequences, and Policy Implications 263, 286 (M. Belinda Tucker & Claudia Mitchell-Kernan eds., 1995).
276. Elizabeth I. Johnson & Jane Waldfogel, Children of Incarcerated Parents: Multiple Risks and Children's Living Arrangements, in Imprisoning America: The Social Effects of Mass Incarceration, at 87, 98.
277. James P. Lynch & William J. Sabol, Effects of Incarceration on Informal Social Control in Communities, in Imprisoning America: The Social Effects of Mass Incarceration, at 135, 135-60.
278. Bruce Western et al., Incarceration and the Bonds Between Parents in Fragile Families, in Imprisoning America: The Social Effects of Mass Incarceration, at 21, 21-43.
279. For a recent, prominent reflection on the way such generalizations about black men have affected African American communities, see Remarks on the Verdict in State of Florida v. George Zimmerman, 2013 Daily Comp. Pres. Doc. 509 (July 19, 2013).
280. See William J. Stuntz, Race, Class, and Drugs, 98 Colum. L. Rev. 1795, 1825-30 (1998) (discussing the effects of community perceptions of unfairness on compliance with the law).
281. See, e.g., Richard Couzens, Evidence-Based Practices: Reducing Recidivism to Increase Public Safety.
282. A Cooperative Effort by Courts and Probation 10 (2011), available at http://www.courts.ca.gov/documents/EVIDENCE-BASEDPRACTICES-Summary-6-27-11.pdf.
283. See, e.g., Margareth Etienne, Legal and Practical Implications of Evidence-Based Sentencing by Judges, 1 Chap. J. Crim. Just. 43 (2009).
284. Matthew Kleiman, Using Evidence-Based Practices in Sentencing Criminal Offenders, in 44 The Book of the States 299 (Council of State Gov'ts ed., 2012), available at http://knowledgecenter.csg.org/drupal/system/ files/matthew_kleiman_2012.pdf.
285. Michael H. Marcus, Conversations on Evidence-Based Sentencing, 1 Chap. J. Crim. Just. 61, 107 (2009).
286. See Rachel E. Barkow, Federalism and the Politics of Sentencing, 105 Colum. L. Rev. 1276, 1278-81 (2005).
287. Analogously, limiting instructions given to juries-instructions to consider evidence for one purpose but not another-are "notoriously ineffective" and "may be counterproductive because they draw jurors' attention to the evidence that is supposed to be ignored. " J.J. Prescott & Sonja Starr, Improving Criminal Jury Decision Making After the Blakely Revolution, 2006 U. Ill. L. Rev. 301, 323 (citing studies).
288. Michael A. Wolff, Evidence-Based Judicial Discretion: Promoting Public Safety Through State Sentencing Reform, 83 N.Y.U. L. Rev. 1389 (2008) (containing a promotion of EBS written by the chair of the Missouri Sentencing Advisory Commission).
289. The mitigation-only approach also would not deprive defendants of standing to challenge EBS.
290. A defendant who would have received diversion to probation had the risk instrument not considered his gender, for instance, is harmed by that consideration. The Supreme Court has often considered equal protection challenges in which the plaintiff claims he was denied a government benefit on the basis of some improper consideration. See, e.g., Fisher v. Univ. of Tex. at Austin, 133 S. Ct. 2411 (2013) (reviewing a challenge based on the denial of state university admission).
291. See William J. Stuntz, Unequal Justice, 121 Harv. L. Rev. 1969, 1974 (2008).
292. James Forman, Jr., Why Care About Mass Incarceration?, 108 Mich. L. Rev. 993, 1001 (2010) (book review).
293. David J. Cooke & Christine Michie, Limits of Diagnostic Precision and Predictive Utility in the Individual Case: A Challenge for Forensic Practice, 34 Law & Hum. Behav. 259, 259 (2010).
294. See U.S. Census Bureau, Statistical Abstract of the United States: 2012, at 151 tbl.229 (2012), available at http://www.census.gov/compendia/statab/2012/tables/ 12s0229.pdf (showing that blacks and particularly Hispanics have lower high school and college graduation rates than whites).
295. To describe something as a 95% confidence interval for an estimated group mean is to express confidence that for 95% of random samples, the same estimation procedure will produce an interval containing the true group mean for the underlying population.
296. The estimated uncertainties in Table 1 are based on a regression of height on gender using standard Stata postestimation prediction commands. By construction, the uncer tainties are the same for men and women. Another way to estimate a 95% prediction interval for an individual woman is to just ignore the men and give the range within which the middle 95% of the women in your sample fall. This range is marked on Figure 1.
297. David J. Cooke & Christine Michie, Limits of Diagnostic Precision and Predictive Utility in the Individual Case: A Challenge for Forensic Practice, 34 Law & Hum. Behav. 259, 259 (2010).
298. See, e.g., Pari McGarraugh, Note, Up or Out: Why "Sufficiently Reliable" Statistical Risk Assessment Is Appropriate at Sentencing and Inappropriate at Parole, 97 Minn. L. Rev. 1079, 1101-02 (2013) (briefly asserting that gender as well as race "must be purged from the list of inputs" but providing little explanation).
299. See, e.g., Pari McGarraugh, Note, Up or Out: Why "Sufficiently Reliable" Statistical Risk Assessment Is Appropriate at Sentencing and Inappropriate at Parole, 97 Minn. L. Rev. 1079, 1101-02 (2013) (briefly asserting that gender as well as race "must be purged from the list of inputs" but providing little explanation).
300. See R. Karl Hanson & Philip D. Howard, Individual Confidence Intervals Do Not Inform Decision-Makers About the Accuracy of Risk Assessment Evaluations, 34 Law & Hum. Behav. 275, 278-79 (2010).
301. Henry M. Hart, Jr. & Albert M. Sacks, The Legal Process: Basic Problems in the Making and Application of Law 1374-80 (William N. Eskridge, Jr. & Philip P. Frickey eds., 1994).
302. See Douglas Mossman, Assessing Predictions of Violence: Being Accurate About Accuracy, 62 J. Consulting & Clinical Psychol. 783, 785-90 (1994) (describing the method as well as competing approaches).
303. See Office of Prob. & Pretrial Servs., Admin. Office of the U.S. Courts, An Overview of the Federal Post Conviction Risk Assessment 9 (2011), available at http://www.uscourts.gov/uscourts/FederalCourts/PPS/PCRA_Sep_2011.pdf.
304. M. Dolan & M. Doyle, Violence Risk Prediction: Clinical and Actuarial Measures and the Role of the Psychopathy Checklist, 177 Brit. J. Psychiatry 303, 305-07 (2000).
305. M. Dolan & M. Doyle, Violence Risk Prediction: Clinical and Actuarial Measures and the Role of the Psychopathy Checklist, 177 Brit. J. Psychiatry 303, 305-07 (2000).
306. See Douglas Mossman, Assessing Predictions of Violence: Being Accurate About Accuracy, 62 J. Consulting & Clinical Psychol. 783, 785-90 (1994) (describing the method as well as competing approaches).
307. This is estimated in the same 400-person sample used above.
308. Note that a 95% confidence interval for an individual for "tall" would run from 0 to 1 for both men and women.
309. one could not be anywhere close to 95% confident that any given woman would be short, or that any given man would be tall. In the sample, 17.5% of women and 82.5% of men were "tall. "
310. The square of this correlation coefficient is one variant on the "pseudo R-squared" statistic, a "fit" measure. This and several other variants could be used to assess a model's ability to explain individual variation, although none should be interpreted as a measure of the overall quality of the model. For a concise summary, see FAQ: What Are Pseudo RSquareds?, Inst. Digital Res. & Educ., http://www.ats.ucla.edu/stat/mult_pkg/faq/ general/Psuedo_RSquareds.htm (last updated Oct. 20, 2011).
311. See Paul Gendreau et al., A Meta-Analysis of the Predictors of Adult Offender Recidivism: What Works!, 34 Criminology 575, 587 tbl.4 (1996).
312. David J. Cooke & Christine Michie, Limits of Diagnostic Precision and Predictive Utility in the Individual Case: A Challenge for Forensic Practice, 34 Law & Hum. Behav. 259, 259 (2010).
313. Henry M. Hart, Jr. & Albert M. Sacks, The Legal Process: Basic Problems in the Making and Application of Law 1374-80 (William N. Eskridge, Jr. & Philip P. Frickey eds., 1994).
314. Kelly Hannah-Moffat, Actuarial Sentencing: An "Unsettled" Proposition, 30 Just. Q. 270, 279-84 (2013).
315. Mark D. Cunningham & Thomas J. Reidy, Violence Risk Assessment at Federal Capital Sentencing: Individualization, Generalization, Relevance, and Scientific Standards, 29 Crim. Just. & Behav. 512, 517 (2002).
316. see also Jessica M. Tanner, "Continuing Threat" to Whom?: Risk Assessment in Virginia Capital Sentencing Hearings, 17 Cap. Def. J. 381, 402-05 (2005) (similarly arguing that "all scientifically derived expertise... is derived from collective data" and that probabilities based on groups can be very useful in formulating individual predictions).
317. Richard E. Redding, Evidence-Based Sentencing: The Science of Sentencing Policy and Practice, 1 Chap. J. Crim. Just. 1, 1 & n.4, 5-6 (2009) (reviewing articles praising EBS and stating that failure to employ EBS "constitutes sentencing malpractice and professional incompetence").
318. Paul E. Meehl, Clinical Versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence 138 (1954).
319. Jennifer L. Skeem & John Monahan, Current Directions in Violence Risk Assessment, 20 Current Directions Psychol. Sci. 38, 40 (2011).
320. William M. Grove & Paul E. Meehl, Comparative Efficiency of Informal (Subjective, Impressionistic) and Formal (Mechanical, Algorithmic) Prediction Procedures: The Clinical-Statistical Controversy, 2 Psychol. Pub. Pol'y & L. 293, 305-06 (1996).
321. In the Russian roulette hypothetical, the decisionmaker is given the only variable that matters: the number of bullets. The recidivism models are not in the same ballpark.
322. William M. Grove & Paul E. Meehl, Comparative Efficiency of Informal (Subjective, Impressionistic) and Formal (Mechanical, Algorithmic) Prediction Procedures: The Clinical-Statistical Controversy, 2 Psychol. Pub. Pol'y & L. 293, 305-06 (1996).
323. 429 U.S. 190, 201 (1976).
324. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 1399-402 (2011) (listing variables in eighteen different instruments).
325. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 1399-402 (2011) (listing variables in eighteen different instruments).
326. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 1399-402 (2011) (listing variables in eighteen different instruments).
327. see also Joan Petersilia & Susan Turner, Guideline-Based Justice: Prediction and Racial Minorities, in 9 Crime and Justice: A Review of Research 151, 153-54, 160 (Don M. Gottfredson & Michael Tonry eds., 1987) (describing sentencing reformers' objective of eliminating role of "status factors" such as employment).
328. see also Joan Petersilia & Susan Turner, Guideline-Based Justice: Prediction and Racial Minorities, in 9 Crime and Justice: A Review of Research 151, 153-54, 160 (Don M. Gottfredson & Michael Tonry eds., 1987) (describing sentencing reformers' objective of eliminating role of "status factors" such as employment).
329. See Douglas Mossman, Assessing Predictions of Violence: Being Accurate About Accuracy, 62 J. Consulting & Clinical Psychol. 783, 785-90 (1994) (describing the method as well as competing approaches).
330. One exception is a 2009 North Carolina report that found that eliminating three potentially problematic variables (race, gender, and prior arrests not resulting in conviction) from the state's risk prediction instrument would have little effect on its predictive accuracy. N.C. Sentencing & Policy Advisory Comm'n, Research Findings and Policy Recommendations from the Correctional Program Evaluations, 2000-2008, at 13 (2009), available at http://www.nccourts.org/Courts/CRS/Councils/spac/Documents/ correctionalevaluation_0209.pdf.
331. Patricia M. Harris, What Community Supervision Officers Need to Know About Actuarial Risk Assessment and Clinical Judgment, Fed. Probation, Sept. 2006, at 8, 9-11 (summarizing literature supporting superiority of actuarial approaches).
332. William M. Grove et al., Clinical Versus Mechanical Prediction: A Meta-Analysis, 12 Psychol. Assessment 19, 22-24 (2000) (listing studies).
333. William M. Grove et al., Clinical Versus Mechanical Prediction: A Meta-Analysis, 12 Psychol. Assessment 25, (2000) (listing studies).
334. Grant T. Harris et al., Prospective Replication of the Violence Risk Appraisal Guide in Predicting Violent Recidivism Among Forensic Patients, 26 Law & Hum. Behav. 377 (2002).
335. Michael A. Wolff, Evidence-Based Judicial Discretion: Promoting Public Safety Through State Sentencing Reform, 83 N.Y.U. L. Rev. 1389 (2008) (containing a promotion of EBS written by the chair of the Missouri Sentencing Advisory Commission).
336. Grant T. Harris et al., Prospective Replication of the Violence Risk Appraisal Guide in Predicting Violent Recidivism Among Forensic Patients, 26 Law & Hum. Behav. 378 (2002).
337. Grant T. Harris et al., Prospective Replication of the Violence Risk Appraisal Guide in Predicting Violent Recidivism Among Forensic Patients, 26 Law & Hum. Behav. 379 (2002).
338. Grant T. Harris et al., Prospective Replication of the Violence Risk Appraisal Guide in Predicting Violent Recidivism Among Forensic Patients, 26 Law & Hum. Behav. 381 (2002).
339. Henry M. Hart, Jr. & Albert M. Sacks, The Legal Process: Basic Problems in the Making and Application of Law 1374-80 (William N. Eskridge, Jr. & Philip P. Frickey eds., 1994).
340. William M. Grove et al., Clinical Versus Mechanical Prediction: A Meta-Analysis, 12 Psychol. Assessment 22, (2000) (listing studies).
341. Terrill R. Holland et al., Comparison and Combination of Clinical and Statistical Predictions of Recidivism Among Adult Offenders, 68 J. Applied Psychol. 203, 207 (1983) (finding that individual decisionmakers better predict violent recidivism, but actuarial prediction instruments better predict some measures of overall recidivism).
342. James Smith & Richard I. Lanyon, Prediction of Juvenile Probation Violators, 32 J. Consulting & Clinical Psychol. 54, 56 (1968) (finding that a juvenile recidivism base expectancy table was slightly more accurate than the predictions of two clinical assessors, but was less accurate than simply predicting that everyone would recidivate).
343. Daniel Glaser, The Efficacy of Alternative Approaches to Parole Prediction, 20 Am. Soc. Rev. 283, 285-86 (1955) (capitalization altered).
344. Daniel Glaser, The Efficacy of Alternative Approaches to Parole Prediction, 20 Am. Soc. Rev. 285, (1955) (capitalization altered).
345. Problems like this recur in other actuarial-versus-clinical studies as well. These studies state a sample size consisting of the number of subjects and calculate statistical significance as though all of the observations were independent. This approach is misleading because there are usually a far smaller number of clinical decisionmakers involved in the study, meaning that standard errors should instead be calculated with clustering on the decisionmaker.
346. J. Stephen Wormith & Colin S. Goldstone, The Clinical and Statistical Prediction of Recidivism, 11 Crim. Just. & Behav. 3, 30 (1984).
347. J. Stephen Wormith & Colin S. Goldstone, The Clinical and Statistical Prediction of Recidivism, 11 Crim. Just. & Behav. 29, (1984).
348. J. Stephen Wormith & Colin S. Goldstone, The Clinical and Statistical Prediction of Recidivism, 11 Crim. Just. & Behav. 17-21, (1984).
349. Howard R. Sacks, Promises, Performances, and Principles: An Empirical Study of Parole Decisionmaking in Connecticut, 9 Conn. L. Rev. 347, 402-03 (1977).
350. J.C. Oleson et al., Training to See Risk: Measuring the Accuracy of Clinical and Actuarial Risk Assessments Among Federal Probation Officers, Fed. Probation, Sept. 2011, at 52, 54-55.
351. J.C. Oleson et al., Training to See Risk: Measuring the Accuracy of Clinical and Actuarial Risk Assessments Among Federal Probation Officers, Fed. Probation, Sept. 2011, at 53-54.
352. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 1399-402 (2011) (listing variables in eighteen different instruments).
353. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 54-55 (2011) (listing variables in eighteen different instruments).
354. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 54-55 (2011) (listing variables in eighteen different instruments).
355. One could frame the state interest as being about the efficient use of finite incarceration resources to maximize crime prevention effects. Unless states have reached their prison capacities and cannot expand, though, I assume that the incarceration rate isn't fixed, so sentencing judges don't think about incarceration of one defendant as trading off with incarceration of another. Instead, they think about whether the particular sentence in question is worth its costs.
356. This assumption may not be true. Some defendants have families that are affected, for instance.
357. A related concern is that the length of incarceration may be a confounding variable in the underlying predictive model. If the people who have one set of characteristics tend to get longer sentences than those with other characteristics, then the comparison of their recidivism rates could be apples to oranges, because one group's rate is the average after, say, three years of incarceration and the other group's rate is the average after five. We thus don't even know from the models who is the riskiest today, much less who is the riskiest X or Y number of years from now.
358. See Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age 78-80 (2007) (reviewing state parole practices).
359. See Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age 78-80 (2007) (reviewing state parole practices).
360. See Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age 78-80 (2007) (reviewing state parole practices).
361. see also Bernard E. Harcourt, A Reader's Companion to Against Prediction: A Reply to Ariela Gross, Yoram Margalioth, and Yoav Sapir on Economic Modeling, Selective Incapacitation, Governmentality, and Race, 33 Law & Soc. Inquiry 265, 269 (2008) (explaining that traditional actuarial methods do not measure comparative elasticities of different groups to policing).
362. This incapacitation effect should be discounted for crime in prison, a complication I will bracket for simplicity.
363. Sean Rosenmerkel et al., Bureau of Justice Statistics, U.S. Dep't of Justice, Felony Sentences in State Courts, 2006-Statistical Tables 6 tbl.1.3 (2009), available at http://www.bjs.gov/content/pub/pdf/fssc06st.pdf.
364. E.g., Roger K. Warren, Evidence-Based Sentencing: The Application of Principles of Evidence-Based Practice to State Sentencing Practice and Policy, 43 U.S.F. L. Rev. 585, 594 (2009).
365. Michael A. Wolff, Lock 'Em Up and Throw Away the Key? Cutting Recidivism by Analyzing Sentencing Outcomes, 20 Fed. Sent'g Rep. 320, 320 (2008).
366. See, e.g., Pari McGarraugh, Note, Up or Out: Why "Sufficiently Reliable" Statistical Risk Assessment Is Appropriate at Sentencing and Inappropriate at Parole, 97 Minn. L. Rev. 1079, 1101-02 (2013) (briefly asserting that gender as well as race "must be purged from the list of inputs" but providing little explanation).
367. See, e.g., Or. Dep't of Corr., The Effectiveness of Community-Based Sanctions in Reducing Recidivism 18-19 (2002), available at http://www.oregon.gov/doc/ CC/docs/pdf/effectiveness_of_sanctions_version2.pdf.
368. For a useful recent review of this literature, see David S. Abrams, The Imprisoner's Dilemma: A Cost-Benefit Approach to Incarceration, 98 Iowa L. Rev. 905, 929-36 (2013).
369. See, e.g., Ilyana Kuziemko, Going Off Parole: How the Elimination of Discretionary Prison Release Affects the Social Cost of Crime 13-22 (Nat'l Bureau of Econ. Research, Working Paper No. 13380, 2007), available at http://www.nber.org/papers/w13380.pdf.
370. For a useful recent review of this literature, see David S. Abrams, The Imprisoner's Dilemma: A Cost-Benefit Approach to Incarceration, 98 Iowa L. Rev. 936, (2013).
371. For a useful recent review of this literature, see David S. Abrams, The Imprisoner's Dilemma: A Cost-Benefit Approach to Incarceration, 98 Iowa L. Rev. 936, (2013).
372. For a useful recent review of this literature, see David S. Abrams, The Imprisoner's Dilemma: A Cost-Benefit Approach to Incarceration, 98 Iowa L. Rev. 936, (2013).
373. For a useful recent review of this literature, see David S. Abrams, The Imprisoner's Dilemma: A Cost-Benefit Approach to Incarceration, 98 Iowa L. Rev. 936, (2013).
374. See, e.g., Rucker Johnson & Steven Raphael, How Much Crime Reduction Does the Marginal Prisoner Buy?, 55 J.L. & Econ. 275, 297-98 (2012).
375. See, e.g., Ilyana Kuziemko, Going Off Parole: How the Elimination of Discretionary Prison Release Affects the Social Cost of Crime 13-22 (Nat'l Bureau of Econ. Research, Working Paper No. 13380, 2007), available at http://www.nber.org/papers/w13380.pdf.
376. See, e.g., Rucker Johnson & Steven Raphael, How Much Crime Reduction Does the Marginal Prisoner Buy?, 55 J.L. & Econ. 293, (2012).
377. See, e.g., Ilyana Kuziemko, Going Off Parole: How the Elimination of Discretionary Prison Release Affects the Social Cost of Crime 13-22 (Nat'l Bureau of Econ. Research, Working Paper No. 13380, 2007), available at http://www.nber.org/papers/w13380.pdf.
378. Avinash Singh Bhati, Urban Inst., An Information Theoretic Method for Estimating the Number of Crimes Averted by Incapacitation 24 tbl.4.2 (2007), available at http://www.urban.org/uploadedpdf/411478_crimes_averted.pdf (showing estimated male elasticities that were slightly greater than female elasticities in most, but not all, states, and by very small margins).
379. See, e.g., Ilyana Kuziemko, Going Off Parole: How the Elimination of Discretionary Prison Release Affects the Social Cost of Crime 23 (Nat'l Bureau of Econ. Research, Working Paper No. 13380, 2007), available at http://www.nber.org/papers/w13380.pdf.
380. Collecting data over the course of an offender's entire life is unrealistic, but follow-up periods substantially longer than the typical one or two years are needed. Most people eventually desist from crime, and people who have not recidivated for six or seven years (after release, if they were incarcerated) have quite low subsequent recidivism rates. E.g., Megan C. Kurlychek et al., Scarlet Letters and Recidivism: Does an Old Criminal Record Predict Future Offending?, 5 Criminology & Pub. Pol'y 483, 499 (2006). Thus, to study the effect of the first year of incarceration (versus none), eight or ten years of outcome data would probably be fine. The study should simply estimate total crime by each individual over a fixed period of time beginning at sentencing, conditional on (among other things) the share of that time that is spent in prison.
381. That measure would incorporate both incapacitation and specific-deterrence effects.
382. See, e.g., Malenchik v. State, 928 N.E.2d 564, 573 (Ind. 2010).
383. Matthew Kleiman, Using Evidence-Based Practices in Sentencing Criminal Offenders, in 44 The Book of the States 299 (Council of State Gov'ts ed., 2012), available at http://knowledgecenter.csg.org/drupal/system/ files/matthew_kleiman_2012.pdf.
384. David E. Patton, Guns, Crime Control, and a Systemic Approach to Federal Sentencing, 32 Cardozo L. Rev. 1427, 1456 (2011).
385. See, e.g., 18 U.S.C. § 3553(a)(2)(C) (2012) (instructing federal courts to consider "protect[ing] the public from further crimes of the defendant").
386. Bearden v. Georgia, 461 U.S. 660, 671 (1983).
387. See Chapman v. California, 386 U.S. 18, 23-24, 26 (1967).
388. 133 S. Ct. 2411, 2434 (2013) (Ginsburg, J., dissenting).
389. Fisher v. Univ. of Tex. at Austin, 631 F.3d 213, 262 (5th Cir. 2011) (Garza, J., specially concurring) (internal quotation marks omitted).
390. Fisher v. Univ. of Tex. at Austin, 631 F.3d 213, 262 (5th Cir. 2011) (Garza, J., specially concurring) (internal quotation marks omitted).
391. Fisher v. Univ. of Tex. at Austin, 631 F.3d 213, 262 (5th Cir. 2011) (Garza, J., specially concurring) (internal quotation marks omitted).
392. Regents of the Univ. of Cal. v. Bakke, 438 U.S. 265, 315 (1978) (opinion of Powell, J.) (internal quotation mark omitted).
393. See, e.g., Model Penal Code: Sentencing § 6B.09 cmt. a at 53, 55 (Tentative Draft No. 2, 2011).
394. see, e.g., Lynn S. Branham, Follow the Leader: The Advisability and Propriety of Considering Cost and Recidivism Data at Sentencing, 24 Fed. Sent'g Rep. 169, 169 (2012).
395. See, e.g., Richard P. Kern & Mark H. Bergstrom, A View from the Field: Practitioners' Response to Actuarial Sentencing; An "Unsettled" Proposition, 25 Fed. Sent'g Rep. 185 (2013) (containing a promotion of EBS written by directors of the Virginia Criminal Sentencing Commission and Pennsylvania Commission on Sentencing).
396. Michael H. Marcus, Conversations on Evidence-Based Sentencing, 1 Chap. J. Crim. Just. 61, 107 (2009).
397. Denise Meyerson, Risks, Rights, Statistics and Compulsory Measures, 31 Sydney L. Rev. 507, 513-15 (2009).
398. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 1373 (2011) (listing variables in eighteen different instruments).
399. David E. Patton, Guns, Crime Control, and a Systemic Approach to Federal Sentencing, 32 Cardozo L. Rev. 1427, 1456 (2011).
400. Jennifer Skeem, Risk Technology in Sentencing: Testing the Promises and Perils, 30 Just. Q. 297, 298 (2013).
401. This is perhaps a particularly realistic possibility with respect to race because of its absence from the instruments: if judges currently implicitly take race into account in predicting recidivism risk, it is possible that giving them a statistical prediction that is not race specific could cause them to stop doing so. Thus, even if EBS increases the weight given to socioeconomic variables that are correlated with race, it could reduce the weight given to race itself, offsetting or even reversing its expected effect on racial disparity.
402. Jordan M. Hyatt et al., Follow the Evidence: Integrate Risk Assessment into Sentencing, 23 Fed. Sent'g Rep. 266, 266 (2011).
403. Kelly Hannah-Moffat, Actuarial Sentencing: An "Unsettled" Proposition, 30 Just. Q. 270, 279-84 (2013).
404. See Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age 78-80 (2007) (reviewing state parole practices).
405. E.g., Kathryn M. Campbell, Expert Evidence from "Social" Scientists: The Importance of Context and the Impact on Miscarriages of Justice, 16 Can. Crim. L. Rev. 13, 14 (2011).
406. Robert L. Kane, Creating Practice Guidelines: The Dangers of Over-Reliance on Expert Judgment, 23 J.L. Med. & Ethics 62, 63 (1995).
407. Michael H. Shapiro, Updating Constitutional Doctrine: An Extended Response to the Critique of Compulsory Vaccination, 12 Yale J. Health Pol'y L. & Ethics 87, 128-29 (2012) (discussing the problem of judicial overreliance on expert claims of causation).
408. Janine Pierson, Comment, Construing Crane: Examining How State Courts Have Applied Its Lack-of-Control Standard, 160 U. Pa. L. Rev. 1527, 1550-53 (2012) (discussing jury overreliance on expert testimony about dangerousness in civil commitment hearings).
409. see also Charles D. Case, Problems in Judicial Review Arising from the Use of Computer Models and Other Quantitative Methodologies in Environmental Decisionmaking, 10 B.C. Envtl. Aff. L. Rev. 251, 256 (1982) (describing courts' and policymakers' tendency to overrely on models and perceived expertise in the environmental context).
410. Robert E. Schween & Steven P. Larson, Groundwater Modeling: Capabilities and Limitations, Use and Abuse, 32 Rocky Mtn. Min. L. Inst. 22-1 (1986) (same).
411. See Thomas M. Hardiman, Foreword, 49 Duq. L. Rev. 637, 637 (2011) ("Any preconceived notions that a judge may have about sentencing upon taking the bench are quickly dwarfed by the awesome responsibility it entails. ").
412. D. Brock Hornby, Speaking in Sentences, 14 Green Bag 2d 147, 157 (2011).
413. J.C. Oleson, Risk in Sentencing: Constitutionally Suspect Variables and Evidence-Based Sentencing, 64 SMU L. Rev. 1399-402 (2011) (listing variables in eighteen different instruments).
414. Robert Pratt, The Implications of Padilla v. Kentucky on Practice in United States District Courts, 31 St. Louis U. Pub. L. Rev. 169, 169 (2011) ("Sentencing is unquestionably the most difficult job of any district court judge. ").
415. This point may help to explain the continuing heavy weight federal judges give to the Federal Sentencing Guidelines, even though they are no longer required to follow them.
416. Gregory A. Huber & Sanford C. Gordon, Accountability and Coercion: Is Justice Blind when It Runs for Office?, 48 Am. J. Pol. Sci. 247, 261 (2004) (finding a positive correlation between sentence length and the approach of elections).
417. Kelly Hannah-Moffat, Actuarial Sentencing: An "Unsettled" Proposition, 30 Just. Q. 270, 279-84 (2013).
418. Kelly Hannah-Moffat, Actuarial Sentencing: An "Unsettled" Proposition, 30 Just. Q. 270, 279-84 (2013).
419. See, e.g., Atul Gawande, The Checklist Manifesto: How to Get Things Right 158-62 (2009).
420. Analogously, limiting instructions given to juries-instructions to consider evidence for one purpose but not another-are "notoriously ineffective" and "may be counterproductive because they draw jurors' attention to the evidence that is supposed to be ignored. " J.J. Prescott & Sonja Starr, Improving Criminal Jury Decision Making After the Blakely Revolution, 2006 U. Ill. L. Rev. 301, 323 (citing studies).
421. Cass R. Sunstein et al., Predictably Incoherent Judgments, 54 Stan. L. Rev. 1153, 1170-71 (2002).
422. Students' comments after completing the exercise supported this interpretation.
423. Michael A. Wolff, Lock 'Em Up and Throw Away the Key? Cutting Recidivism by Analyzing Sentencing Outcomes, 20 Fed. Sent'g Rep. 320, 320 (2008).
424. Subjects who were given William's case first gave significantly higher sentences to both defendants than those who were given Robert's case first. But order did not significantly affect the relative sentences given or the effect of the RRPI.
425. E.g., Kathryn M. Campbell, Expert Evidence from "Social" Scientists: The Importance of Context and the Impact on Miscarriages of Justice, 16 Can. Crim. L. Rev. 13, 14 (2011).
426. See Bernard E. Harcourt, Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age 78-80 (2007) (reviewing state parole practices).
427. Scholarly criticism has focused on procedural concerns-mainly the prisoner's lack of counsel at parole hearings. For this reason, the commentary to the revised MPC claims to "'domesticate[]' the use of risk assessments by repositioning them in the open forum of the courtroom"-that is, by using them in sentencing instead of in parole (which the MPC seeks to abolish entirely). Model Penal Code: Sentencing § 6B.09 cmt. a at 54 (Tentative Draft No. 2, 2011).
428. See, e.g., Pari McGarraugh, Note, Up or Out: Why "Sufficiently Reliable" Statistical Risk Assessment Is Appropriate at Sentencing and Inappropriate at Parole, 97 Minn. L. Rev. 1079, 1101-02 (2013) (briefly asserting that gender as well as race "must be purged from the list of inputs" but providing little explanation).
429. See, e.g., Pari McGarraugh, Note, Up or Out: Why "Sufficiently Reliable" Statistical Risk Assessment Is Appropriate at Sentencing and Inappropriate at Parole, 97 Minn. L. Rev. 1079, 1101-02 (2013) (briefly asserting that gender as well as race "must be purged from the list of inputs" but providing little explanation).
430. Indeed, risk is arguably the only legitimate parole consideration, because considerations such as retributive justice or general deterrence have already been considered by the sentencing judge. The only reason to leave the sentence indeterminate is to account for the fact that recidivism risk may evolve over time.
431. Those who believe risk prediction is an improper basis for punishment should simply oppose indeterminate sentencing. Cf. Christopher Slobogin, Prevention as the Primary Goal of Sentencing: The Modern Case for Indeterminate Dispositions in Criminal Cases, 48 San Diego L. Rev. 1127, 1128-30 (2011).
432. Note that while the Supreme Court once labeled parole an "act of grace, " the deprivation of which a prisoner could not contest, this theory is now considered "longdiscredited. " Samson v. California, 547 U.S. 843, 864 n.5 (2006) (Stevens, J., dissenting) (internal quotation marks omitted).
433. Bd. of Pardons v. Allen, 482 U.S. 369, 377 n.8, 378 n.10 (1987).
434. Morrissey v. Brewer, 408 U.S. 471, 482 (1972).
435. See, e.g., Pamela M. Casey et al., Nat'l Ctr. for State Courts, Using Offender Risk and Needs Assessment Information at Sentencing: Guidance for Courts from a National Working Group (2011), available at http://www.ncsc.org/ ~/media/Files/PDF/Services%20and%20Experts/Areas%20of%20expertise/Sentencing%20P robation/RNA%20Guide%20Final.ashx.
436. Melissa Aubin, The District of Oregon Reentry Court: An Evidence-Based Model, 22 Fed. Sent'g Rep. 39, 42 (2009) (discussing evidence-based practice in federal reentry courts).
437. Roger K. Warren, Nat'l Ctr. for State Courts, Evidence-Based Practice to Reduce Recidivism: Implications for State Judiciaries (2007), at 155-56. available at http://static.nicic.gov/Library/023358.pdf.