Predicting Erroneous Convictions: A Social Science ...

1 The author(s) shown below used Federal funds provided by the Department of Justice and prepared the following final report: Document Title: Predicting Erroneous Convictions: A Social Science Approach to Miscarriages of Justice Author(s): Jon B. Gould, Julia Carrano, Richard Leo, Joseph Young Document No.: 241389 Date Received: February 2013 Award Number: 2009-IJ-CX-4110 This report has not been published by the Department of Justice. To provide better customer service, NCJRS has made this Federally-funded grant report available electronically. Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the Department of Justice. Predicting Erroneous Convictions: A Social Science Approach to Miscarriages of Justice Jon B. Gould American University 4400 Massachusetts Avenue, Washington, 20016 202-885-6535 (phone) 202-885-6536 (fax) Julia Carrano American University 4400 Massachusetts Avenue, Washington, 20016 202-885-6421 (phone) 202-885-6536 (fax) Richard Leo University of San Francisco School of Law 2130 Fulton Street San Francisco, CA 94117 415-661-0162 (phone) 415-661-0172 (fax) Joseph Young American University 4400 Massachusetts Avenue, Washington, 20016 202-885-2618 (phone) 202-885-6536 (fax)

2 With Katie Hail-Jares Kevin Maass Andrea Butler Erin Crites Jaclyn Menditch Sarah Ohlsen Truman Morrison George Mason University Ren e Nicole Souris American University December 2012 This project was conducted under Grant No. 2009-IJ-CX-4110 awarded by the National Institute of Justice, Office of Justice Programs, United States Department of Justice. Points of view in this document are solely those of the authors and do not necessarily represent the official position or policies of the United States Government. This document is a research report submitted to the Department of Justice.

3 This report has not been published by the Department. Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the Department of ABSTRACT The last thirty years have seen an enormous increase not only in the exonerations of innocent defendants but also academic scholarship on Erroneous convictions. This literature has identified a number of common factors that appear frequently in Erroneous conviction cases, including forensic error, prosecutorial misconduct, false confessions, and eyewitness misidentification. However, without a comparison or control group of cases, researchers risk labeling these factors as causes of Erroneous convictions when they may be merely correlates. In fact, the only way to establish what causes an Erroneous conviction is to understand which factors are exclusive to Erroneous convictions as against other sets of cases.

4 This approach has been taken by only a handful of scholars, all of whom have been interested in what separates Erroneous convictions from other convictions. Missing so far in the literature is a study that asks how the criminal justice system identifies innocent defendants in order to prevent Erroneous convictions. What we want to know and thus what dictated our research strategy is what factors are uniquely present in cases that lead the system to rightfully acquit or dismiss charges against the innocent defendant (so-called near misses ), which are not present in cases that lead the system to erroneously convict the innocent. If we understand this, then we are closer to comprehending what policy interventions can influence the justice system to prevent future Erroneous convictions. Our study employed a mixed methods approach that involved both quantitative and qualitative analysis. We began by identifying a set of 460 Erroneous conviction and near miss cases that met a stringent definition of innocence.

5 We then researched and coded the cases along a number of variables, including location effects, nature of the victim, nature of the defendant, facts available to the police and prosecutor, quality of work by the criminal justice system, and This document is a research report submitted to the Department of Justice. This report has not been published by the Department. Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the Department of quality of work by the defense. The cases were subsequently analyzed using bivariate and logistic regression techniques. With the assistance of an expert panel, we also explored the cases from a qualitative perspective and examined the statistical results in light of this exploration. The results indicate that 10 factors the age and criminal history of the defendant, the punitiveness of the state, Brady violations, forensic error, a weak defense and prosecution case, a family defense witness, an inadvertent misidentification, and lying by a non-eyewitness help explain why an innocent defendant, once indicted, ends up erroneously convicted rather than released.

6 Other factors traditionally suggested as sources of Erroneous convictions, including false confessions, criminal justice official error, and race effects, appear in statistically similar rates in both sets of cases; thus, they likely increase the chance that an innocent suspect will be indicted but not the likelihood that the indictment will result in a conviction. Finally, our qualitative review of the cases reveals how the statistically significant factors are connected and exacerbated by tunnel vision, which prevents the system from self-correcting once an error is made. In fact, tunnel vision provides a useful framework for understanding the larger system-wide failure that separates Erroneous convictions from near misses. Among the policy implications of our findings is that increased attention to the failing dynamics of the criminal justice system, rather than simply isolated errors or causes, may lead to better prevention of Erroneous convictions.

7 In addition, our results suggest that there should be greater emphasis at all levels and on all sides of the criminal justice system, including police, prosecutors, defense attorneys and judges, to analyze and learn from past mistakes before they result in serious miscarriages of justice. To this end, we encourage continued research on near misses among both practitioners and scholars. This document is a research report submitted to the Department of Justice. This report has not been published by the Department. Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the Department of TABLE OF CONTENTS ABSTRACT .. ii TABLE OF CONTENTS .. iv EXECUTIVE SUMMARY .. xi I. INTRODUCTION .. 1 Background .. 1 I. B. What Do We Know Now? Factors that Correlate with Erroneous 7 Mistaken Eyewitness Identification .. 7 False Confessions.

8 9 Tunnel Vision .. 15 Perjured Informant Testimony .. 16 Forensic Error .. 16 Prosecutorial Error .. 19 Inadequate Defense Representation .. 20 Interrelated Themes .. 20 New Methods: Social Science Research on What Distinguishes Erroneous Convictions from Other Cases .. 23 Social Science Approaches .. 25 Investigating a New Question: How Erroneous Convictions Differ from Near Misses .. 29 How Does the Criminal Justice System Identify Innocence Before Conviction? .. 31 This document is a research report submitted to the Department of Justice. This report has not been published by the Department. Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the Department of Hypotheses: Comparing Erroneous Convictions and Near Misses .. 32 II. 38 Case Criteria .. 38 Identifying Qualifying Cases .. 41 Data Collection and Case Coding.

9 45 The Narrative Coding Document .. 45 Sources of Case Facts .. 46 Coding Case Facts into SPSS .. 50 Rating the Strength of Cases .. 51 Introduction to the Police Foundation Rating Scale .. 51 Applying the Police Foundation Rating Scale to Project Cases .. 52 III. QUANTITATIVE ANALYSIS .. 57 Frequencies and Bivariate Analysis .. 57 Statistically Significant Variables .. 57 Variables That Do Not Distinguish the Cases .. 60 Logistic 64 Estimating the Models .. 64 Regression Results .. 66 Prediction/Forecasting .. 70 IV. QUALITATIVE ANALYSIS AND THE EXPERT PANEL .. 72 This document is a research report submitted to the Department of Justice. This report has not been published by the Department. Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the Department of Convening the Expert Panel .. 72 Qualitative Analysis.

10 74 Predictor #1: Prior Convictions .. 74 Predictor #2: Intentional Misidentification .. 76 Predictor #3: Forensic Errors .. 76 Predictor #4: Weak Prosecution Case .. 78 Predictor #5: Weak Defense Case .. 81 The Role of the 83 How Factors Interact: Tunnel Vision .. 84 V. DISCUSSION .. 89 Broader Interpretation .. 90 Recommendations for Reform .. 94 Study Limitations .. 101 Possible Directions for Future Research .. 104 VI. REFERENCES .. 107 VII. TABLES AND FIGURES .. 120 Figure 1. Distribution of Cases by County Population .. 120 Table 1. Erroneous Convictions by State .. 121 Table 2. Near Misses by State .. 123 Table 3. Bivariate Results: All Variables, Location Effects .. 125 This document is a research report submitted to the Department of Justice. This report has not been published by the Department. Opinions or points of view expressed are those of the author(s) and do not necessarily reflect the official position or policies of the Department of Table 4.