Faculty and Staff

We integrate multiple domains of psychological science to identify, better understand, and manage the effects of subtle but powerful biases in forensic mental health assessment. This topic is ripe for discussion, as research evidence that challenges our objectivity and credibility garners increased attention both within and outside of psychology. We begin by defining bias and provide rich examples from the judgment and decision making literature as they might apply to forensic assessment tasks. The cognitive biases we review can help us explain common problems in interpretation and judgment that confront forensic examiners. This leads us to ask (and attempt to answer) how we might use what we know about bias in forensic clinicians’ judgment to reduce its negative effects.

We conducted an international survey in which forensic examiners who were members of professional associations described their two most recent forensic evaluations (N=434 experts, 868 cases), focusing on the use of structured assessment tools to aid expert judgment. This study describes:

1. The relative frequency of various forensic referrals.
2. What tools are used globally.
3. Frequency and type of structured tools used.
4. Practitioners’ rationales for using/not using tools.

We provide general descriptive information for various referrals. We found most evaluations used tools (74.2%) and used several (on average 4). We noted the extreme variety in tools used (286 different tools). We discuss the implications of these findings and provide suggestions for improving the reliability and validity of forensic expert judgment methods. We conclude with a call for an assessment approach that seeks structured decision methods to advance greater efficiency in the use and integration of case-relevant information.

This chapter integrates from cognitive neuroscience, cognitive psychology, and social psychology the basic science of bias in human judgment as relevant to judgments and decisions by forensic mental health professionals. Forensic mental health professionals help courts make decisions in cases when some question of psychology pertains to the legal issue, such as in insanity cases, child custody hearings, and psychological injuries in civil suits. The legal system itself and many people involved, such as jurors, assume mental health experts are “objective” and untainted by bias. However, basic psychological science from several branches of the discipline suggest the law’s assumption about experts’ protection from bias is wrong. Indeed, several empirical studies now show clear evidence of (unintentional) bias in forensic mental health experts’ judgments and decisions. In this chapter, we explain the science of how and why human judgments are susceptible to various kinds of bias. We describe dual-process theories from cognitive neuroscience, cognitive psychology, and social psychology that can help explain these biases. We review the empirical evidence to date specifically about cognitive and social psychological biases in forensic mental health judgments, weaving in related literature about biases in other types of expert judgment, with hypotheses about how forensic experts are likely affected by these biases. We close with a discussion of directions for future research and practice.

The current debate over graduate rate calculations and results has glossed over the relationship between student migration and the accuracy of various graduation rates proposed over the past five years. Three general grade-based graduation rates have been proposed recently, and each has a parallel version that includes an adjustment for migration, whether international, internal to the U.S., or between different school sectors. All of the adjustment factors have a similar form, allowing simulation of estimates from real data, assuming different unmeasured net migration rates. In addition, a new age-based graduation rate, based on mathematical demography, allows the simulation of estimates on a parallel basis using data from Virginia's public schools.

Both the direct analysis and simulation demonstrate that graduation rates can only be useful with accurate information about student migration. A discussion of Florida's experiences with longitudinal cohort graduation rates highlights some of the difficulties with the current status of the oldest state databases and the need for both technical confidence and definitional clarity. Meeting the No Child Left Behind mandates for school-level graduation rates requires confirmation of transfers and an audit of any state system for accuracy, and basing graduation rates on age would be a significant improvement over rates calculated using grade-based data.