Evidence-Based Sentencing and the Taint of Dangerousness
Today’s world is “all about the data.”1 In a variety of contexts, innovators have offered statistical models as a way to reduce or eliminate human error.2 The promise of quantitative optimization has even influenced our criminal justice system. About twenty states have developed or adopted models to predict a defendant’s risk of recidivating.3 Often used in bail determinations and pretrial diversion programs, these risk predictors are also increasingly used for sentencing.4 In these cases, defendants may actually receive harsher sentences if they are calculated to have a higher risk of committing future crimes.5
Proponents argue that future risk, as predicted by past data, is nothing new to sentencing.6 Creating the Federal Sentencing Guidelines, for example, involved analyzing “some 40,000 convictions [and] a sample of 10,000 augmented presentence reports.”7 Modern risk models are just more accurate “tool[s]”8 that improve the calculations judges already make about defendants.9 Plus, consideration of the models’ risk “scores” is not even mandatory.10
This Essay, however, joins the call for greater scrutiny of “evidence-based sentencing,”11 and cautions against a particular form. In Pennsylvania, the state legislature has mandated the creation of a “risk assessment instrument” for determining “the relative risk that an offender will reoffend and be a threat to public safety.”12 The model incorporates not only factors related to criminal conduct, but also demographic traits: age, gender, and county.13 As scholars like Sonja Starr have pointed out, these types of factors raise concerns under the Equal Protection Clause;14 at a minimum, their inclusion could aggravate existing disparities in incarceration.15 Yet surprisingly, Pennsylvania’s decision has drawn scant public attention.16 Indeed, the hearings designed to elicit feedback were so poorly attended that the state sentencing commission extended the public comment period through the end of 2015.17 Given the high stakes, this lack of attention is quite troubling.
This Essay seeks to foster debate by highlighting an additional problem18: legislatively tailored risk models that single out “high risk” people could violate the Constitution’s prohibition on bills of attainder.19 Bills of attainder are “legislative enactment[s] which determine guilt and inflict punishment upon an identifiable person or group without a judicial trial.”20Historically, the term “bill of attainder” referred to a legislative death sentence.21 However, the Supreme Court has instructed that the Bill of Attainder Clauses are “not to be given a narrow historical reading.”22 The reason is that the prohibitions on state23 and federal24 bills of attainder are vital safeguards of the separation of powers,25 preventing legislatures from exercising the judicial power to “rul[e] upon the blameworthiness of, and levy appropriate punishment upon, specific persons.”26 Thus, “[l]egislative acts, no matter what their form, that apply either to named individuals or to easily ascertainable members of a group in such a way as to inflict punishment on them without a judicial trial are bills of attainder prohibited by the Constitution.”27
Although Pennsylvania’s risk predictor is not a traditional bill of attainder, there are important similarities. First, creating and adopting Pennsylvania’s model was a “legislative act.”28 The Pennsylvania General Assembly amended Title 42 of Pennsylvania’s Code of Law (Judiciary and Judicial Procedure) to require the development of a “sentence risk assessment instrument.”29 The instrument was then designed by the state sentencing commission, which unlike the Federal Sentencing Commission,30 is a legislative agency31 that includes “[t]wo members of the House of Representatives” and “[t]wo members of the Senate.”32 The Commission’s purpose, as explained in Commonwealth v. Sessoms, is “the furtherance of . . . the legislative power.”33
Second, one could argue that Pennsylvania’s risk predictor “inflicts punishment without a judicial trial.”34 Whether consideration at sentencing is mandatory or not, legislative pronouncements that certain individuals deserve35 greater punishment could significantly shape sentencing outcomes.36 In addition, the consequences of being labelled “a threat to public safety” extend well beyond the criminal justice system. Adopting a risk model “endors[es] a message: that [the state] considers certain groups of people dangerous.”37 Historically,38 certain bills of attainder—called “bills of pains and penalties”39—did exactly this, imposing “brand[s] of infamy”40 on their targets to create “broader social disability.”41 For example, such bills humiliated people by having them locked in the public stocks.42 In addition to public shame, the targeted could suffer concrete private disabilities, such as discrimination in employment and housing; the result could be “a kind of social death.”43 Today, condemnation and censure continue to be important touchstones for bills of attainder: “[w]hile the prohibition against bills of attainder has evolved far beyond the original context of capital sentences, it continues to focus on legislative enactments that ‘set a note of infamy’ on the persons to whom the statute applies.”44 Branding the possessors of “high-risk” traits could have such an effect.
Finally, and most importantly, statistical risk predictors enable a legislature to “single out its enemies—or the politically unpopular.”45 By selecting the right traits from among the many that are correlated with recidivism, a legislature could deliberately taint a particular group.46 Modern risk models include a wide range of “static” factors that individuals cannot change or escape47—from criminal and substance-abuse history to demographic information to the criminality of one’s associates and friends.48 Even factors like whether one’s parents have been arrested or jailed have been found to predict recidivism;49 building these types of criteria into a risk model could entrench community-wide or generational taints of dangerousness. For instance, Pennsylvania’s model includes three demographic traits, concluding that “offenders were more likely to recidivate if they were young, . . . male, [and] from an urban county.”50 These traits, alone, may not specify a sufficiently narrow group,51 but the use of these types of factors opens the door to alarming possibilities. As the Justice Department warned, evidence-based sentencing “will become much more concerning over time as other far reaching sociological and personal information . . . are incorporated into risk tools.”52 Pennsylvania has taken another major step in the direction of Big Data.53
Alexander Hamilton cautioned that “[n]othing is more common than for a free people, in times of heat and violence, to gratify momentary passions, by letting into the government principles and precedents which afterwards prove fatal to themselves [including] banishment by acts of the legislature.”54 There is no doubt that crime reduction and efficient resource allocation are worthy goals of the criminal justice system. This Essay, however, is a reminder that, in our pursuit of solutions, we cannot lose sight of important constitutional principles.55 Thus, in Pennsylvania and beyond, we should move into the “Risk Assessment Era” with care and, crucially, deliberation. This is especially true in states that have adopted or are adopting risk models, but also in states that may join the trend someday; citizens should investigate these reforms, attend public hearings, and voice their opinions. For even worse than sacrificing our values would be losing them through inattention and indifference.
Gregory Cui is a member of the Yale Law School J.D. Class of 2017. He is deeply grateful to Professor Daniel Markovits, Professor Jed Rubenfeld, Michael Clemente, Charlie Bridge, Elizabeth Ingriselli, Joseph Masterman, David Simins, Tal Eisenzweig (his outstanding Lead Editor), McKaye Neumeister, Megan McGlynn, William Stone, Rachel Wilf, and Alina Lindblom for their thoughtful comments and suggestions.
Preferred Citation: Gregory Cui, Evidence-Based Sentencing and the Taint of Dangerousness, 125 Yale L.J. F. 315 (2016), http://www.yalelawjournal.org/forum/evidence-based-sentencing-and-the-taint-of-dangerousness.