Title: In re: Proportionality Review Project

State: new-jersey

Issuer: New Jersey Supreme Court

Document:

(This syllabus is not part of the opinion of the Court. It has been prepared by the Office of the Clerk for the convenience of the reader. It has been neither reviewed nor approved by the Supreme Court. Please note that, in the interests of brevity, portions of any opinion may not have been summarized). PORITZ, C.J., writing for a majority of the Court. This matter completes the second phase in a two-part review of the Court's proportionality review procedures undertaken pursuant to the Court's Order in State v. Loftin, 157 N.J. 253 (1999) (Loftin II). In Loftin II, the Court found that the proportionality review methodologies it had been using were seriously flawed and that the time had come for a careful reconsideration. The Court appointed the Honorable David S. Baime as Special Master, who submitted a report on the first phase of the project regarding the size of the universe, individual proportionality review, and the consolidation of direct death penalty appeals and proportionality review in April 1999 (Baime Report I). This Court issued determinations regarding those issues in In re: Proportionality Review Project, 161 N.J. 71 (1999) (Proportionality Review I). Special Master Baime issued Baime Report II in December 1999, dealing with questions pertaining to systemic proportionality review _ whether ethnic, racial or gender bias exists in the administration of our capital sentencing laws. The Court has considered briefs submitted by the Attorney General, the Public Defender, and amici curiae, and heard oral argument. HELD: The Court adopts the recommendations of Special Master Baime outlined in the Baime Report II with certain modifications. 1. The first statistical models conceived by Professor David C. Baldus, the Court's Special Master appointed to oversee the development of a proportionality review system, were not designed to test for racial discrimination. Nonetheless, defendants have alleged racial discrimination in the administration of the death penalty premised largely on those statistical models. The Court acknowledged previously that the multiple regression models devised by Baldus contained fundamental problems that precluded reliance on their results. The Court therefore requested retired Judge Richard Cohen, with the assistance of a renowned statistician, to assess the models and report to the Court. Judge Cohen informed the Court that the multiple regressions were inherently unreliable because they were not parsimonious. (A statistical model with a relatively small number of well-crafted parameters is known as a parsimonious model). There were a relatively small number of cases in the databases, and the regression models contained an excessive number of variables. The need for further work led the Court to charge its present Special Master, Judge Baime, to attempt to develop parsimonious statistical models and consider whether a panel of judges should be appointed to independently verify the culpability ratings derived from the models. (Pp. 3-10). 2. Judge Baime proposes a monitoring system that rests on the assumption that no single method is sufficiently reliable to provide convincing evidence of a race effect in death penalty sentencing. He therefore recommends a multifaceted approach consisting of bivariate analysis, regression studies, case exploratory analysis, and a precedent-seeking type review. Bivariate analysis is a simple comparison between race and outcome. Its utility is limited because it does not take other factors into account. Nonetheless, the Court will include bivariate analysis because of its simplicity and because it could help shed some light on any relationship between race and sentencing outcome. (Pp. 10-13) 3. Creating reliable multiple regression models has been the biggest challenge in systemic proportionality review. Such models must have a limited number of independent variables to be parsimonious. Judge Baime proposes a multi-step methodology in which one defines the base set of variables; explores the bivariate relationship between the race variable and each variable in the base set; excludes variables that do not have a statistically significant relationship with a race variable; and estimates the regression model including only those variables that reached a statistically significant threshold. Multiple regressions have an advantage not shared by bivariate techniques. Multiple regressions control for other factors and therefore isolate the effects of race on capital sentencing. The new parsimonious regressions are expected to be more reliable than the models developed initially. The Court remains mindful, however, that the small number of cases in the database requires caution when using multiple regressions in systemic review. (Pp. 13-22) 4. The third component of the proposed monitoring system is the use of case-sorting techniques or exploratory case analysis, under which the data is broken into various combinations of statutory and nonstatuory factors that have a statistically significant impact on death sentencing. A precedent-seeking type of review must then be conducted on categories with strong racial disparities to determine whether the defendants' culpability levels explain the outcome. The Public Defender has raised valid questions about exploratory case analysis. The Court understands this is a new approach that will require decisions on the part of the Special Master in implementation. The Court will consider those decisions and whether the approach is potentially useful after the system is put in place. (Pp. 22-25) 5. The Court cannot rely on the results of any single methodology showing a race effect. In order for a defendant to relentlessly document the risk of racial disparity in the imposition of the death penalty, the standard required to establish disproportionality, there must be a consistent finding using multiple techniques. (Pp. 26-28) 6. It is anticipated that the Special Master will annually update the database and the modes of analysis in the monitoring system, and that he will present a summary review to the Public Defender and Attorney General for comments. If the Court concludes in one case that a defendant has not met his burden of establishing systemic disproportionality, it will rely on that conclusion in subsequent cases and until the next yearly adjustment to the data. (Pp. 28-29) 7. In three proportionality review cases before the Court this term (Harris, Feaster, and Morton), defendants have alleged systemic racial discrimination. Judge Baime has examined the available data under a hybrid of methodologies used in previous proportionality reviews and under approaches he recommends for use in the future. He finds no reliable statistical evidence that the race of the defendant influences death sentencing. The Court agrees that the risk of racial discrimination has not been relentlessly documented by the models. A bivariate analysis, however, suggests that killers of white victims are more likely to be capitally prosecuted than killers of black victims. Because other methodologies do not corroborate those results, and because bivariate analysis does not account for other factors that may influence death-sentencing decisions, the Court does not find that race has operated as an impermissible factor in the imposition of the death penalty. (Pp. 30-31) The AOC and the Special Master are directed to implement the monitoring system approved in this decision. JUSTICE LONG, concurring in part and dissenting in part, agrees with the Court's ruling with one exception - the requirement that a defendant present relentless documentation that race influenced his death sentence. She believes the relentless documentation standard is without basis in law or reason and that its application subverts the entire process. JUSTICES O'HERN, STEIN, COLEMAN, and LaVECCHIA join in CHIEF JUSTICE PORITZ's opinion. JUSTICE VERNIERO joins in the opinion with the exception of Section V, which addresses individual proportionality review cases in which he is not participating. JUSTICE LONG has filed a separate opinion concurring in part and dissenting in part. IN RE: PROPORTIONALITY REVIEW PROJECT (II) Argued February 15, 2000 -- Decided August 2, 2000 On review of the Recommendations of Special Master. Claudia Van Wyk, Deputy Public Defender, II, and Mordecai D. Garelick, Assistant Deputy Public Defender, argued the cause on behalf of the Office of the Public Defender (Ivelisse Torres, Public Defender, attorney). Catherine A. Foddai, Deputy Attorney General, argued the cause on behalf of the Attorney General of New Jersey (John J. Farmer, Jr., Attorney General, attorney). Lawrence S. Lustberg, submitted a brief on behalf of amici curiae Association of Criminal Defense Lawyers of New Jersey and New Jersey State Conference of NAACP Branches (Gibbons, Del Deo, Dolan, Griffinger & Vecchione, attorneys). The opinion of the Court was delivered by PORITZ, C.J. This matter completes the second phase in a two-part review of the Court's proportionality review procedures undertaken pursuant to our Order in State v. Loftin, 157 N.J. 253, 454-55 (Loftin II), cert. denied, ___ U.S. ___, 120 S. Ct. 229, 145 L. Ed. 2d 193 (1999). In Loftin II, the Court found that the proportionality review methodologies we had been using were seriously flawed and that the time had come for a careful reconsideration of our approach to the proportionality phase of death penalty review. Id. at 286. We established a process for reconsideration that included the designation of a Special Master and the submission of a report to the Court covering four discrete areas of concern: the size of the universe of comparison cases; particular issues in respect of individual proportionality review; questions relating to the statistical models used in both individual and systemic proportionality review; and the status of proportionality review as a separate proceeding in death penalty appeals. Ibid. The first phase of this project was completed on April 28, 1999, when the Honorable David S. Baime, a Presiding Judge of the Appellate Division appointed Special Master for the Supreme Court, submitted an initial report wherein he made findings and recommendations regarding the size of the universe, individual proportionality review and the models used for that purpose, and the feasibility of consolidating direct death penalty appeals with proportionality review. See David S. Baime, Report to the New Jersey Supreme Court: Proportionality Review Project (Apr. 28, 1999) (Baime Report I); see also In re Proportionality Review Project, 161 N.J. 71 (1999) (Proportionality Review I) (adopting Baime Report I with modifications). Upon our consideration of Baime Report I, this Court issued determinations regarding those issues in August 1999, thus establishing baseline procedures for individual proportionality review. On December 1, 1999, Special Master Baime issued Report to the New Jersey Supreme Court: Systemic Proportionality Review Project (Dec. 1, 1999) (Baime Report II). Baime Report II, as its name suggests, deals with questions pertaining to systemic proportionality review, that is, whether ethnic, racial or gender bias exists in the administration of our capital sentencing laws. Id. at 1. After reviewing briefs submitted by the Attorney General, the Public Defender and amici curiae, Association of Criminal Defense Lawyers of New Jersey and New Jersey State Conference of NAACP Branches, and on hearing oral argument, we adopt Baime Report II with modifications as outlined in this opinion. race variables [were included] in the culpability models to ensure that variables for legitimate case characteristics were not carrying any possible race effects. It was in the course of this work that we observed the race effects . . . . Because discrimination was not the primary mandate in this project, we consider these results to be strictly preliminary. More work will be required to determine if they persist under closer scrutiny and alternative analyses, to determine, for example, whether they are statistical artifacts or flukes, and to assess their legal and practical significance. [David C. Baldus, Death Penalty Proportionality Review Project: Final Report to the New Jersey Supreme Court 100-01 (Sept. 24, 1991) (Baldus Report).] Despite this disclaimer, defendants have alleged racial discrimination in the administration of the death penalty premised largely on the statistical models created by Professor Baldus. Last term, after having conducted four proportionality reviews between 1992 and 1995, we evaluated the reliability of the multiple regression models Baldus had devised.See footnote 11 Loftin II, supra, 157 N.J. at 308-16; see also State v. DiFrisco, 142 N.J. 148 (1995) (DiFrisco III) (using Baldus-created models for proportionality review), cert. denied, 516 U.S. 1129, 116 S. Ct. 949, 133 L. Ed. 2d 873 (1996); State v. Martini, 139 N.J. 3 (1994) (Martini II) (same), cert. denied, 516 U.S. 875, 116 S. Ct. 203, 113 L. Ed. 2d 137 (1995); State v. Bey, 137 N.J. 334 (1994) (Bey IV) (same), cert. denied, 513 U.S. 1164, 115 S. Ct. 1131, 130 L. Ed. 2d 1093 (1995); Marshall II, supra (same). We acknowledged that the models contained fundamental problems that precluded reliance on their results and that those problems were due both to intrinsic and extrinsic factors, i.e., the structure or design of the models themselves and too few cases. Loftin II, supra, 157 N.J. at 310-15. We had asked retired Judge Richard Cohen, with the assistance of a world-renowned statistician, Dr. John W. Tukey,See footnote 22 to assess those factors and others, and to report back to the Court within a short period of time. Id. at 302-03. See generally Richard S. Cohen, Report to the Supreme Court of New Jersey (Jan. 27, 1997) (Cohen Report). Because the Public Defender claimed that the models demonstrated an impermissible race effect, understanding the nature and impact of the models' shortcomings was critical. Judge Cohen informed us that the multiple regressions were inherently unreliable because they were not parsimonious, a requisite for a reliable regression. Loftin, supra, 157 N.J. at 310-11. A statistical model with a relatively small number of well-crafted parameters is known as a parsimonious model. Id. at 311. The lack of parsimony in the Baldus models created a risk of overfitting, possibly resulting in the false attribution of effect to a variable. Id. at 311 n.13. In other words, considering the relatively small number of cases in the database, particularly death-sentenced cases, the regression models contained an excessive number of variables and, thus, statistical results suggesting racial discrimination may have reflected a methodological flaw rather than reality. Id. at 311. In addition to the problems related to the small size of the database, various data-coding decisions appeared to affect the model results in unintended and inappropriate ways. Id. at 311 12. Those concerns about the validity of the statistical models were substantiated by the disparate ratings of capitally prosecuted defendants derived from the regressions and from culpability rankings produced by a survey of judges conducted for comparison purposes. Id. at 310. In short, the exploratory work of Judge Cohen, and the advice of Dr. Tukey, demonstrated the need for a more thorough consideration of parsimonious models, further exploration of other methodologies, and a good hard look at some of our earlier decisions about data-coding classifications. Loftin II provides a detailed review of these matters. Id. at 302-16. Suffice it to say here that the need for further work led to a detailed charge to our present Special Master, Judge Baime, that included consideration of both individual and systemic proportionality review. Id. at 453-57. In addition to a review of data-coding choices and the projected size of the database over time, we specifically ordered that (6) [t]he Special Master shall attempt to develop parsimonious statistical models for more reliable regression studies of race effect and shall consider whether the process of purging, i.e., the removal of the indirect effects of race from variables that appear to be unrelated to race, produces results that are useful; [and that] (7) [t]he Special Master shall consider Special Master Cohen's recommendation, submitted in State v. Loftin, supra, that the Court appoint a panel of judges to perform periodic assessments of penalty-trial outcomes, along with the composition and mandate of such an independent judicial panel, as independent verification of the culpability ratings derived from the models. In Baime Report II, Judge Baime addressed those questions and others. With assistance from Professors David WeisburdSee footnote 33 and Joseph Naus,See footnote 44 he considered new approaches to the study of systemic discrimination and made recommendations to the Court. We consider Judge Baime's systemic recommendations in this opinion. Before we begin our discussion of the Special Master's report, however, some further reflection on systemic review is in order. The study of system-wide discrimination requires the use of statistical techniques in complex socio-political settings. The process is far more complicated than counting the number of defendants by race and the number of death penalties meted out, although it certainly includes such elementary comparative analyses. A myriad of discretionary decisions are made at every level in the system, and sorting out their relationship to the race of either defendants or victims is complex and difficult. We have learned that statistical modeling for that purpose is largely untested and that its usefulness is uncertain. The improvements and additions we approve today will need further review down the road. We make these choices because we know of no other means by which the relationship, if any, between race and the death penalty system in New Jersey may be reviewed. The importance of understanding whether racial discrimination infects our system of capital punishment requires that we make this effort. [T]he monitoring system I propose rests on the assumption that there is no single method that is sufficiently reliable to provide convincing evidence of a race effect in death penalty sentencing. I recommend a multifaceted approach consisting of bivariate analysis, regression studies, case exploratory analysis, and a precedent-seeking type review of the cases. We analyze each component of the proposed system in turn. The difficulty, then, lies in choosing a smaller set of variables that is consistent with those criteria. Professors Weisburd and Naus have concluded that a statistical approach makes the most sense, and Judge Baime agrees. Id. at 39 (quoting Weisburd and Naus Report at 27) (internal quotations omitted). Accordingly, Judge Baime proposes a multi-step methodology to be applied at three points in the system -- to juries' sentencing decisions, to the entire death-eligible universe, and to death eligible cases that advance to penalty trial. Id. at 40. We summarize: (1) Define the base set of variables, which could be limited to statutory factors or could also include variables selected in a survey conducted for that purpose. (2) Explore the bivariate relationship between the race variable and each variable in the base set. (3) Exclude variables that do not have a statistically significant relationship with a race variable using a threshold of .05 for the death-eligible universe and .10 for the penalty-trial universe. (4) Estimate the regression model including only those variables that have reached the thresholds . . . plus the relevant race variables. Id. at 41.See footnote 66 If that regression is not parsimonious, raise the threshold for statistical significance. See id. at 40-42. Judge Baime believe[s] that this approach can produce a more parsimonious set of regression models for monitoring race effects, although he also cautions that the omission of variables that are important indicators of outcome would not only render[] the model incapable of explaining the outcome sought, but [would] distort[] the effects of the variables which have been included. Id. at 42. We accept the advice of the Special Master and his consultants that, when properly designed, a series of multiple regressions can be a valuable technique for measuring whether race affects the death-penalty system in this state. Moreover, multiple regression techniques have an advantage not shared by bivariate techniques: multiple regressions control for other factors and therefore isolate the effects of race on capital sentencing. Put another way, unlike bivariate analyses, in assessing race effects multiple regressions account for differences in defendants' culpability, thereby permitting a more complex understanding of the relationships between the variables examined. Other issues related to the selection of variables for the models remain. Professor Naus suggests that the base set of variables should be limited to statutory aggravating and mitigating factors plus race-of-victim and race-of-defendant variables. Id. at 20. Although that might be [t]he most obvious and simplest solution, ibid., Judge Baime cautions that excluding relevant nonstatutory variables could impair the regression model's utility and mask a race effect that exists or attribute race bias when there is none. Id. at 21. Professor Weisburd recommends including nonstatutory variables within the base set by surveying judges, prosecutors, defense attorneys, and scholars to identify those factors that may influence capital-sentencing decisions. Id. at 22. The Public Defender advocates including former jurors in this survey. Our concerns about the exclusion of pertinent variables impels us to conclude that the base set of variables must include nonstatutory, as well as statutory, factors. See Marshall II, supra, 130 N.J. at 157 ( If we did not consider the circumstances of the case beyond the c(4) aggravating factors in a search for disproportionality, we would be ignoring the reality of the situation . . . . ). This decision comports with our previous recognition that nonstatutory factors may determine whether a defendant is prosecuted capitally or sentenced to death. See, e.g., Chew II, 159 N.J. 183, 210-14 (considering nonstatutory factors to compare defendants' culpability in precedent-seeking approach to individual proportionality review), cert. denied, ___ U.S. ___, 120 S. Ct. 593, 145 L. Ed. 2d 493 (1999). We make the choice to include non-statutory factors because we believe that the exclusion of relevant variables would undermine the reliability of the models. Although we consider it unnecessary to survey prosecutors, defense attorneys, scholars, and former jurors in order to identify the variables that will be used, we do approve asking judges with experience trying capital cases to select the nonstatutory factors that are most relevant to death sentencing decisions. Even if parsimonious multiple regressions do not omit relevant variables, the technique presents other problems that must be confronted. Where there is inconsistent coding of variables or interdependence between cases in the database (i.e., the outcome in one case affects the outcome of another case), the reliability of the results is adversely affected. Regression analysis assumes that these defects are not present. Professors Weisburd and Naus make a number of recommendations for improving the reliability of the regressions. Among other things, they advise that compliance with the independence-of-cases assumption requires no more than one case for each defendant in the universe. Baime Report II at 17-18. To ensure consistency in the coding of variables, they recommend that the Administrative Office of the Courts (AOC) code statutory-factor variables in all cases, including those in the penalty-trial universe. Id. at 29. They also suggest examining whether changes in the use and interpretations of the c(4)(c) (torture or depravity) aggravating factor and the c(5)(h) (catch all) mitigating factor may have fundamentally altered the meaning of those variables such that their coding is no longer valid. Id. at 24. Lastly, they would do away with Baldus's factor analysis methodology -- providing weights for defining new variables -- and they would code race variables more precisely and more consistently to distinguish between whites, blacks, Latinos, Asians, and others. Id. at 22-23. We begin with the consultants' last suggestion in respect of the coding of race. Previously, the race-of-defendant variable was coded either black or nonblack, and the race-of-victim variable was coded white or nonwhite. Judges Cohen and Baime and their statistical advisors have universally criticized this coding practice. We agree with their concerns and order that both defendants and victims be coded as white, black, Hispanic, Asian, or other. That coding change would increase accuracy, be more complete, and recognize the racial and ethnic diversity that exists in New Jersey. We accept the principle that adoption of a method for defining a single case for each defendant for regression analysis is appropriate. Id. at 18. Thus, for example, Joseph Harris committed five death-eligible murders in two separate incidents. Only one of those murders will be included in the database even though to do so cuts against our instincts in respect of a defendant who murdered more than one victim in separate incidents. We rely, in making this decision, on the experts who tell us the models require that there be no systematic relationship between measured characteristics of the cases and unmeasured characteristics that influence death penalty sentencing. Ibid. We recognize that there is inconsistency in the coding of variables. The use and interpretation of the c(4)(c) (torture or depravity) aggravating factor and c(5)(h) (catch-all) mitigating factor have changed over time. Also, having AOC staff members code the presence or absence of aggravating and mitigating factors obviously differs from having a jury decide whether aggravating and mitigating factors are present. We have given those issues some thought and have decided, for now, to tolerate certain inconsistencies inherent in the system. In part, we are reluctant to substitute AOC staff members' judgments based on a cold record for the findings of a jury that sat through and participated in a capital trial.See footnote 77 We expect that the new parsimonious regressions will be more reliable than the models developed by our first Special Master. Nonetheless, we are mindful still of the relatively small number of cases in the database. The Court has remarked before on the reluctance of New Jersey juries to impose the ultimate penalty except in the most egregious cases. See Marshall II, supra, 130 N.J. at 192-94. Over the last five years, jurors have sentenced only ten defendants to death. It is ironic indeed that the result of juror reluctance to impose the death penalty is a database too small for reliable statistical analysis of race effects. That small database, and the risk of violating one or more of the assumptions upon which multiple regressions are based, requires us to act cautiously when we consider the use of multiple regressions in systemic review. Judge Baime also suggests that the sorting approach could be simplified through use of the salient-factors categories used in individual proportionality review, i.e., that cases be classified by their most important aggravating attribute. Id. at 44-45. For the purposes of systemic proportionality review, each salient-factors-test category could be tested for the presence of a race effect. Id. at 45. We adopt Judge Baime's recommendation to include the sorting approach when the AOC and the Special Master provide the next set of models to the Public Defender and the Attorney General. Explanatory case analysis may assist in classifying cases and determining whether there are racial disparities in any of the case categories. We recognize that the sorting approach can examine only two variables at a time and does not control for other variables that may influence death-sentencing decisions. Consequently, like simple bivariate analyses, the results may be confounded by unexamined factors. Also, sorting can be unwieldy because there are so many possible combinations of statutory and nonstatutory factors, although we understand that only those combinations that have a statistically significant correlation with the imposition of a death sentence will be analyzed. See id. at 45. Because the Public Defender has raised valid questions about the sorting methodology, particularly in connection with the combinations of factors, we will proceed step-by-step. Exploratory case analysis is a new approach to systemic review. We leave its administration to the Special Master who, we expect, will draw on the expertise of his statistical consultants. We know that decisions must be made as the technique is refined. We will consider those decisions in our first proportionality review after the new system is put in place. In that review we will evaluate whether the sorting approach is potentially useful and whether it should or should not be continued as part of the monitoring system. We find no reliable statistical evidence that the race of the defendant influences death sentencing either at the penalty trial stage or in the larger death-eligible sample of cases. Nor does the statistical evidence support the thesis that the race of the defendant affects which cases progress to a penalty trial. Further, the statistical evidence suggests that the race of [the] victim does not affect death-sentencing rates -- killers of white victims are no more likely to receive the death penalty than killers of non-white victims. Finally application of our monitoring system discloses no consistent statistical evidence indicating that the race of the victim affects which cases progress to a penalty trial. However, some of the evidence in that respect is conflicting, and the issue should be revisited when the database increases. We agree that the risk of racial discrimination has not been relentlessly documented by the models. We are concerned, however, that a bivariate analysis suggests that killers of white victims are more likely to be capitally prosecuted than killers of black victims. Because other methodologies do not corroborate those results, and because a bivariate analysis by definition does not account for other factors that may influence death sentencing decisions, we cannot find that race has operated as an impermissible factor in the imposition of the death penalty. Loftin II, supra, 157 N.J. at 346. IN RE PROPORTIONALITY REVIEW PROJECT. __________________________ LONG, J., concurring in part and dissenting in part. No one can fault this Court for its commitment to systemic proportionality review. We have not stinted in our efforts and have incorporated newly developed and more reliable models in our quest when prior models failed to do the job. This, our latest initiative, is certainly a step in the right direction and, with one exception, I concur in it. That exception is the requirement that a defendant present relentless documentation that race influenced his death sentence. Because I believe that the relentless documentation standard is without basis in law or reason, and that its application subverts the entire process, I dissent from its incorporation into the project. I. When we first took up the issue of racial discrimination and the death penalty, we focused on the risk that defendants will be sentenced to death either because of their race or the race of the victim. State v. Marshall, 130 N.J. 109, 219 (1992) (Marshall II). Because we were committed to determining whether such a risk was present, we became involved in what can only be denominated as a swamp of statistical data. Somehow as we attempted to wend our way through that swamp, our focus shifted from the possibility of racial discrimination to its liklihood. That we were waylaid is not a surprise. What began as an optimistic experiment in social science, has become yet another example of the confusion and frustration that often surrounds social science in the courts. David Weisburd, Good for What Purpose?: Social Science, Race and Proportionality Review in New Jersey, Hebrew University of Jerusalem (visited July 21, 2000) (http://mishpatim.mscc.huji.ac.il/ newsite/CrimeGroup/weisburd/workpap.htm) (printed in Social Science, Social Policy and the Law, Russell Sage, R. Kagan, P. Ewick and A. Sarat eds. (1999)) . Confusion was to be expected. What was not expected was our slow but steady movement from the notion of risk to the notion of certainty in terms of the quantum of evidence necessary to prove race effect. That shift was unfortunate, because our duty to act arises from merely knowing that the death penalty may be meted out according to race; it is not contingent on having before us overwhelming evidence that such an affront to our Constitution has occurred. NO. A-60 IN RE: PROPORTIONALITY REVIEW PROJECT (II) DECIDED