A practical tool for information management in forensic decisions: Using Linear Sequential Unmasking-Expanded (LSU-E) in casework

The effects of cognitive bias, examiner expertise, and stimulus material on forensic evidence analysis

Forensic examiners have come under scrutiny due to high-profile exonerations, highlighting the consequences that contextual bias can have on investigations. Researchers have proposed solutions to reduce the effects of bias including blind testing and redacting task-irrelevant information. Practitioners have concerns over the limitations of some of this research that uses untrained students to examine complex pieces of forensic evidence (e.g., fingerprints) (1; but see 2 for studies including trained experts and/or actual casework). This study sought to (a) examine the effect of contextual bias on examiners' evaluation of forensic evidence by varying the amount of pre-comparison information available to participants, (b) compare student and expert examiners' performance and their vulnerability to contextual bias, and (c) examine the effects of contextual bias on examiners' evaluation of different types of forensic evidence. Expert fingerprint examiners and student participants were presented with varying amounts of pre-comparison case information and compared matching and non-matching fingerprint and footwear impression evidence. Results suggest no effects of blinding examiners from case information or redacting task-irrelevant information. As expected, expert fingerprint examiners were more likely to correctly identify matching fingerprints and correctly exclude non-matching fingerprints than students. However, expert fingerprint examiners were no better than student participants at comparing footwear impression evidence. These findings suggest that sample, stimulus selection, and discipline-specific training matter when investigating bias in forensic decision making. These findings suggest caution when using forensic stimuli with student samples to investigate forensic decision-making and highlight the need for more research on redaction procedures.

Assessing physical fit examinations of stabbed and torn textiles through a large dataset of casework-like items and interlaboratory studies

Several organizations have outlined the need for standardized methods for conducting physical fit comparisons. This study answers this call by developing and evaluating a systematic and transparent approach for examining, documenting, and interpreting textile physical fits, using qualitative feature descriptors and a quantitative metric (Edge Similarity Score, ESS) for the physical fit examination of textile materials. Here, the results from 1027 textile physical fit comparisons are reported. This includes the evaluation of inter and intraanalyst variation when using this method for hand-torn and stabbed fabrics. ESS higher than 80% and ESS lower than 20%, respectively, support fit and nonfit conclusions. The results show that analyst accuracy ranges from 88% to 100% when using this criterion. The estimated false-positive rate for this dataset (2% false positives, 10 of 477 true nonfit pairs) demonstrates the importance of assessing the quality of a physical fit during an examination and reveals that potential errors are low, but possible in textile physical fit examinations. The risk of error must be accounted for in the interpretation and verification processes. Further analysis shows that factors such as the separation method, construction, and design of the samples do not substantially influence the ESS values. Additionally, the proposed method is independently evaluated by 15 practitioners in an interlaboratory exercise that demonstrates satisfactory reproducibility between participants. The standardized terminology and documentation criteria are the first steps toward validating approaches to streamline the peer review process, minimize bias and subjectivity, and convey the probative value of the evidence.

Experimental results on data analysis algorithms for extracting and interpreting edge feature data for duct tape and textile physical fit examinations

A physical fit is an important observation that can result from the forensic analysis of trace evidence as it conveys a high degree of association between two items. However, physical fit examinations can be time-consuming, and potential bias from analysts may affect judgment. To overcome these shortcomings, a data analysis algorithm using mutual information and a decision tree has been developed to support practitioners in interpreting the evidence. We created these tools using data obtained from physical fit examinations of duct tape and textiles analyzed in previous studies, along with the reasoning behind the analysts' decisions. The relative feature importance is described by material type, enhancing the knowledge base in this field. Compared with the human analysis, the algorithms provided accuracies above 90%, with an improved rate of true positives for most duct tape subsets. Conversely, false positives were observed in high-quality scissor cut (HQ-HT-S) duct tape and textiles. As such, it is advised to use these algorithms in tandem with human analysis. Furthermore, the study evaluated the accuracy of physical fits when only partial sample lengths are available. The results of this investigation indicated that acceptable accuracies for correctly identifying true fits and non-fits occurred when at least 35% of a sample length was present. However, lower accuracies were observed for samples prone to stretching or distortion. Therefore, the models described here can provide a valuable supplementary tool but should not be the sole means of evaluating samples.

The most consistent finding in forensic science is inconsistency

The most consistent finding in many forensic science domains is inconsistency (i.e., lack of reliability, reproducibility, repeatability, and replicability). The lack of consistency is a major problem, both from a scientific and a criminal justice point of view. Examining forensic conclusion data, from across many forensic domains, highlights the underlying cognitive issues and offers a better understanding of the issues and challenges. Such insights enable the development of ways to minimize these inconsistencies and move forward. The aim is to highlight the problem, so that it can be minimized and the reliability of forensic science evidence can be improved.

The secret life of crime labs

Houston TX experienced a widely known failure of its police forensic laboratory. This gave rise to the Houston Forensic Science Center (HFSC) as a separate entity to provide forensic services to the City of Houston. HFSC is a very large forensic laboratory and has made significant progress at remediating the past failures and improving public trust in forensic testing. HFSC has a large and robust blind testing program, which has provided many insights into the challenges forensic laboratories face. HFSC's journey from a notoriously failed lab to a model also gives perspective to the resource challenges faced by all labs in the country. Challenges for labs include the pervasive reality of poor-quality evidence. Also that forensic laboratories are necessarily part of a much wider system of interdependent functions in criminal justice making blind testing something in which all parts have a role. This interconnectedness also highlights the need for an array of oversight and regulatory frameworks to function properly. The major essential databases in forensics need to be a part of blind testing programs and work is needed to ensure that the results from these databases are indeed producing correct results and those results are being correctly used. Last, laboratory reports of "inconclusive" results are a significant challenge for laboratories and the system to better understand when these results are appropriate, necessary and most importantly correctly used by the rest of the system.

The scientific reinvention of forensic science

Forensic science is undergoing an evolution in which a long-standing "trust the examiner" focus is being replaced by a "trust the scientific method" focus. This shift, which is in progress and still partial, is critical to ensure that the legal system uses forensic information in an accurate and valid way. In this Perspective, we discuss the ways in which the move to a more empirically grounded scientific culture for the forensic sciences impacts testing, error rate analyses, procedural safeguards, and the reporting of forensic results. However, we caution that the ultimate success of this scientific reinvention likely depends on whether the courts begin to engage with forensic science claims in a more rigorous way.

Reducing the impact of cognitive bias in decision making: Practical actions for forensic science practitioners

Previously published methods for reducing the impact of cognitive bias in forensic decision making have focused primarily on actions at the laboratory or organizational levels. This paper presents generalized and specific actions that forensic science practitioners can take to reduce the impact of cognitive bias in their work. Practical examples illustrating ways that practitioners can implement many of the specific actions are also provided, along with some suggestions for handling court testimony about cognitive bias. The actions presented in this paper provide a means through which individual practitioners can take ownership for minimizing cognitive bias in their work. Such actions can provide supporting evidence to stakeholders that forensic practitioners acknowledge the existence of cognitive bias and its potential influence on their work, and they can also stimulate implementation of methods that focus on solutions at the laboratory and organizational levels.

Proactive crime scene response optimizes crime investigation

The Proactive Crime Scene Response is a technique utilizing targeted forensic analytical results to guide criminal investigations in real time. Analytical value of evidence maximized by forensic laboratories is directly related to the recognition, documentation, collection, and preservation of evidentiary items located at the crime scene. Improved education, coordination and communication between the crime scene investigators and forensic scientist experts creates a seamless analytical process flow, enabling greater focus on high value evidence with decreased response time and greater impact on investigational direction. Real time data from focused forensic analyses and use of databases provides primary investigative leads, with suspect identities, whereabouts at the time of crime commission, links to other crimes and other critical collaborative crime solving information. Case examples highlighting successful application of various aspects of this model will be provided, with recommendations for implementation including Rapid DNA and supporting business cases.