Commonwealth v. (Redacted) , 33 Mass. L. Rptr. 295 ( 2016 )

Lu, John T., J.

On April 1, 2016, a jury found the defendant guilty of two counts of assault and batteiy pursuant to G.L.c. 265, §13A(b)(l). On that day, the court ordered the defendant, pending sentencing, to undergo full-scale Ohio Risk Assessment System (“ORAS”) testing. During this testing, the defendant may exercise his Fifth Amendment rights to not answer certain questions, and may also have his attorney present.¹ The Court takes this opportunity to provide further reasoning for its order.

The ORAS utilizes seven categories of questions to assess an individual: (1) criminal histoiy, (2) education, employment, and financial situation, (3) family and social support, (4) neighborhood problems, (5) substance use, (6) peer associations, and (7) criminal attitudes and behavioral patterns; subsets of neighborhood problems are “high crime area” and “drugs readily available in neighborhood.”² At the conclusion of the assessment, an individual receives a score in each of the categories, as well as an overall assessment classification of either “high,” “moderate,” or “low” risk, with respect to the individual’s likelihood of recidivism. Nevertheless, in light of the ongoing debate over the utility of data-driven systems in sentencing criminal defendants, the court acknowledges that the ORAS results are not singularly determinative of a sentence that the court imposes, but serve as just one factor for the court’s consideration in imposing sentencing.

The court strives to impose a sentence that is proportional to the crime(s) committed, and that achieves a balance between the two competing sentencing philosophies: utilitarianism and retributivism. Under the utilitarian end of the spectrum, a court will impose a sentence as a means to punish an individual in order to protect society, promote deterrence, and rehabilitate the individual.³ On the other hand, a retributivist approach seeks to match a sentence to the individual’s moral culpability and responsibility.⁴ These two philosophies also subsume various theories, such as retribution, deterrence, and rehabilitation, all important considerations for striking the appropriate balance in sentencing. Whether the use of data-driven or risk-assessment systems falls along the utilitarian-retributivist continuum is unsettled, but may indeed skew towards a utilitarian approach.⁵

While utilizing data-driven and evidence-based analyses, like the ORAS, is not foreign to the criminal justice system, applications have traditionally, and successfully, been utilized on back-end applications, such as guiding parole boards’ decisions, rather than on front-end applications, such as sentencing decisions; however, a number of jurisdictions are starting to take these data-driven analyses into account in sentencing. Using risk assessments and data-driven systems has abundant potential to not only make our corrections system more effective, individualize the administration of justice and reduce costs, but also deter recidivism.⁶ Based in social science, “[c]riminological meta-analysis has identified fifteen key variables—encompassed within the seven categories of the ORAS—that are significantly related to recidivism: 1) criminal companions, (2) antisocial personality, (3) adult criminal history, (4) race, (5) pre-adult antisocial behavior, (6) family rearing practices, (7) social achievement, (8) interpersonal conflict, (9) current age, (10) substance abuse, (11) intellectual functioning, (12) family criminality, (13) gender, (14) socio-economic status of origin, and (15) personal distress.⁷

Incorporating a data-driven approach to sentences is attractive to the court for its inherent utility for a number of reasons. Relying on data-based tools is consistent with the court’s historical reliance on empirical approaches, such as its use of the Sentencing Guidelines.⁸ Using a risk assessment instrument, in contrast to a subjective clinical evaluation, further provides an objective estimate of risk of re-offense because it is the product of mathematical scores that classify an individual as having either a high, moderate or low risk of reoffending; it is also purportedly more effective in forecasting recidivism than clinical assessments.⁹ These risk assessments, in theory, yield consistent and reliable results regardless of the individual conducting the assessment.¹⁰ Judicial efficiency is also supported because risk assessment testing provides all judges with identically reported data.¹¹ Finally, risk assessment tests help to reduce the imposition of excessive punishment, which in turn results in reduced incarceration, saved taxpayer monies, and reduced damage to individual offenders, their families, and communities.¹²

Nevertheless, the court is aware of the potential risks, unintended consequences, and prejudicial effects that may occur by incorporating risk assessments and other data-driven systems into sentencing. As former Attorney General Eric Holder has cautioned, with respect to risk assessments,

[. . .] Although these measures were crafted with the best of intentions, I am concerned that they may inadvertently undermine our efforts to ensure individualized and equal justice. By basing sentencing decisions on static factors and immutable characteristics—like the defendant’s education level, socioeconomic background, or neighborhood—they may exacerbate unwarranted and unjust disparities that are already far too common in our criminal justice system and in our society.

Criminal sentences must be based on the facts, the law, the actual crimes committed, the circumstances surrounding each individual case, and the defendant’s history of criminal conduct. They should not be based on unchangeable factors that a person cannot control, or on the possibility of a future crime that has not taken place. Equal justice can only mean individualized justice, with charges, convictions, and sentences befitting the conduct of each defendant and the particular crime he or she commits. ¹³

Generally speaking, the court is mindful that constitutional or statutory limitations—race, sex, religion, socio-economic status, for example—may forbid consideration of certain traits that are the subject of risk-assessment questions.¹⁴ Additionally, certain traits that the ORAS seeks to consider may not be indicative of individual conduct, but rather a larger group of similar individuals.¹⁵ Valid concerns may arise about whether the traits elicited by the assessment truly reflect an individual’s ability to reduce his future likelihood of recidivating because they discount an individual’s potential to develop over time.¹⁶

Under the circumstances in this case, however, the benefits of having the defendant undergo the ORAS for consideration during his sentencing outweigh the potential consequences, and the court is comfortable subjecting the defendant to full-scale testing with safeguards—allowing the defendant to assert Fifth Amendment rights by refusing to answer certain questions and having his attorney present—in place.

1 The court strongly recommends that the defendant’s attorney attend any and all meetings and risk-assessment testing with the probation service.

2 See, e.g., State v. Jennings, 2014 Ohio 2307, ¶26 (2nd District Ohio Court of Appeals, May 30, 2014).

3 See Linda G. Mills, The Justice of Recovery: How the State Can Heal the Violence of Crime, 57 Hastings L.J. 457, 473 (2006); Joshua Dressier, The Wisdom and Morality of Present-Day Criminal Sentencing, 38 Akron L.Rev. 853, 853-54 (2005).

4 See David Dolinko, Three Mistalces of Retrihutivism, 39 UCLA L.Rev. 1623, 1626 (1992) (explaining retributivism as “the claim that what makes the practice of punishment morally permissible is that criminals deserve punishment, regardless of whatever beneficial consequences might flow from imposing that punishment”).

5 “ ‘Evidence-based sentencing’ (EBS) refers to the use of actuarial risk prediction instruments to guide a judge’s sentencing decision. The instruments are designed to assist judges in their pursuit of several traditional utilitarian sentencing objectives—incapacitation, specific deterrence, and rehabilitation—each of which centers on the reduction of the defendant’s future crime risk.” Sonja B. Starr, Evidence-Based. Sentencing and the Scientific Rationalization of Discrimination, 66 Stan.L.Rev. 803, 809 (2014). See also Guyora Binder & Nicholas J. Smith, Framed: Utilitarianism and Punishment of the Innocent, 32 Rutgers L.J. 115, 116 (2000) (“Utilitarian penology treats punishment as a costly instrument of public policy, permissible only when its benefits in reducing future crime outweigh the pain, fear, and public expense it imposes”); Louis Michael Seidman, Soldiers, Martyrs, and Criminals: Utilitarian Theory and the Problem of Crime Control 94 Yale L.J. 315, 320 (1984) (‘Traditionally, utilitarians have begun with the premise that the criminal justice system should minimize the sum of the costs of crime and crime prevention. Since everyone’s welfare is included in the social calculus, the cost of crime prevention includes not only enforcement costs (police) and process costs (courts), but also the suffering imposed upon criminals made to undergo punishment”).

6 See Schall v. Martin, 467 U.S. 253, 279 (1984) (recognizing “that a prediction of future criminal conduct is ‘an experienced prediction based on a host of variables’ which cannot be readily codified”), quoting Greenholtz v. Nebraska Penal Inmates, 442 U.S. 1, 16 (1979).

7 See J.C. Oleson, Risk Assessment at Sentencing, ASU Law Journal (June 20, 2011), available at http://arizonastatelawjoumal.org/risk-assessment-at-sen tencing/.

8 See U.S. Sentencing Guidelines Manual ch. 1, pt. A, subpt. 1(3) (2013) (explaining “Congress first sought honesiy in sentencing” by avoiding “indeterminate sentencejs] of imprisonment,” and “[s]econd, Congress sought reasonable uniformity in sentencing by narrowing the wide disparity in sentences imposed for similar criminal offenses committed by similar offenders”); Massachusetts Sentencing Guidelines, step 3 (1998) (instructing judge to determine “sentencing guidelines range [on guidelines grid] for the offense/offender” by “identifying that grid cell which represents the intersection of the offense seriousness level of the governing offense (vertical axis) and the classification of the criminal history (horizontal axis)”).

9 See John Monahan, A Jurisprudence of Risk Assessment: Forecasting Harm Among Prisoners, Predators, and Patients, 92 VA L.Rev. 391, 406 (2006).

10 Compare Christopher Slobogin, Dangerousness and Expertise, 133 U.Pa.L.Rev. 97, 122-23 (1984), with John C. Coffee, Jr., The Repressed Issues of Sentencing: Accountability, Predictability, and Equality in the Era of the Sentencing Commission, 66 Geo.L.J. 975, 1002 (1978).

11 See Michael A. Wolff, Evidence-Based. Judicial Discretion: Promoting Public Safety Through State Sentencing Reform, 83 N.Y.U.L.Rev. 1389, 1416 (2008) (“ ‘Evidence-based sentencing’ should replace the misunderstood phrase ‘judicial discretion.’ As with many decisions in our courts and in our criminal justice system, discretion is inherent. Instead of removing discretion, we should be prepared to defend our decisions by basing them on evidence . . .”}.

12 See Committee on Causes and Consequences of High Rates of Incarceration: Committee on Law and Justice; Division of Behavioral and Social Sciences and Education; National Research Council; Jeremy Travis, Bruce Western, and Steve Redburn, eds., The Growth of Incarceration in the United States: Exploring Causes and Consequences, The National Academies Press 339 (2014) (positing that “[t]he change in penal policy over the past four decades may have had a wide range of unwanted social costs, and the magnitude of crime reduction benefits is highly uncertain”); Monahan, supra note 7, at 408 (“The general superiority of actuarial over clinical risk assessment in the behavioral sciences has been known for half a century”).

13 Attorney General Eric Holder, Remarks, National Association of Criminal Defense Lawyers 57th Annual Meeting and 13th State Criminal Justice Network Conference (Aug. 1, 2014).

14 See Michael Tonry, Legal and Ethical Issues in the Prediction of Recidivism, Federal Sentencing Reporter, Vol. 26, No. 3, p. 171 (Feb. 2014) (noting most instruments use socioeconomic factors that correlate with race and ethnicity, and include factors that punish people for choices that people are allowed to make in a free society such as whether to get married, live in a stable residence, or have a regular job); Kelly Hannah-Moffat, Actuarial Sentencing: An “Unsettled” Proposition, paper presented at University at Albany Symposium on Sentencing, pp. 14-17 (Sept. 2010) (contending research on assessment instruments has not adequately vetted tools for use on racial minorities, and noting that social context—gender, race, economic and socio-structural factors—plays role in crime, and assessment does not account for these factors; highlighting possibility that minorities might score higher on risk assessments because “of their elevated exposure to risk, racial discrimination, and social inequality—not necessarily because of their criminal propensities or the crimes perpetrated”); Jay P. Singh and Seena Fazel, Forensic Risk Assessment: a Metareview, Criminal Justice and Behavior, Vol. 37, No. 9, p. 978 (Sept. 2010) (highlighting three studies finding the higher the percentage of white offenders in the sample, the higher the predictive validity of the instrument—suggesting that instruments can better predict risk for white offenders).

15 See Roberts v. U.S. Jaycees, 468 U.S. 609, 625 (1984) (concluding that such group-assigned traits “force[ ] individuals to labor under stereotypical notions that often bear no relationship to their actual abilities”); Jessica Pishko, Punished for Being Poor: The Problem with Using Big Data in the Justice System Pac. Standard Mag. (Aug. 18,2014), available at https: //perma.cc/6GP8-RQ 57?type=pdf (“Data always relies on averages. As a result, some people are bound to behave differently than the data predicts . . .”); Daniel S. Goodman, Note, Demographic Evidence in Capital Sentencing, 39 Stan.L.Rev. 499, 522-23 (1987) (objecting to demographic generalization in the capital context that “classifies defendants on the basis of their affiliation with broad social groups, disregarding the fact that individual behavior may deviate substantially from average group behavior”).

16 See Barbara D. Underwood, Law and the Crystal Ball: Predicting Behavior with Statistical Inference and Individualized Judgment, 88 Yale L.J. 1408, 1414(1979) (‘To imprison a person because of the crimes he is expected to commit denies him the opportunity to choose to avoid those crimes . . . [R]espect for individual autonomy requires recognition of the possibility that an individual can choose to refute any prediction about himself’); cf. Pepper v. United States, 562 U.S. 476, 490-93 (2011) (highlighting defendant’s substantial behavioral progress as directly bearing on prediction of likelihood of recidivism).