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.

Accessibility is increasingly used as a metric when evaluating changes to public transport systems. Transit travel times contain variation depending on when one departs relative to when a transit vehicle arrives, and how well transfers are coordinated given a particular timetable. In addition, there is necessarily uncertainty in the value of the accessibility metric during sketch planning processes, due to scenarios which are underspecified because detailed schedule information is not yet available. This article presents a method to extend the concept of "reliable" accessibility to transit to address the first issue, and create confidence intervals and hypothesis tests to address the second.

There is a need for indicators of transportation-land use system quality that are understandable to a wide range of stakeholders, and which can provide immediate feedback on the quality of interactively designed scenarios. Location-based accessibility indicators are promising candidates, but indicator values can vary strongly depending on time of day and transfer wait times. Capturing this variation increases complexity, slowing down calculations. We present new methods for rapid yet rigorous computation of accessibility metrics, allowing immediate feedback during early-stage transit planning, while being rigorous enough for final analyses. Our approach is statistical, characterizing the uncertainty and variability in accessibility metrics due to differences in departure time and headway-based scenario specification. The analysis is carried out on a detailed multi-modal network model including both public transportation and streets. Land use data are represented at high resolution. These methods have been implemented as open-source software running on commodity cloud infrastructure. Networks are constructed from standard open data sources, and scenarios are built in a map-based web interface. We conclude with a case study, describing how these methods were applied in a long-term transportation planning process for metropolitan Amsterdam.