A correct identification can mean the difference between exonerating a criminal or convicting an innocent person. If the examiner has identified a sufficient number of corresponding features to be confident that the two prints came from the same person, then this final “same source” decision is logged into the computer system, which is then typically “verified” by a second examiner who-depending on their jurisdiction-may or may not be blind to the initial examiner’s decision (Thompson, Black, Jain, & Kadane, 2017).ĭespite the fact that fingerprint examiners have been shown to perform exceptionally well (Searston & Tangen, 2017a Searston & Tangen, 2017b Tangen, Thompson, & McCarthy, 2011 Thompson & Tangen, 2014 Ulery, Hicklin, Buscaglia, & Roberts, 2011), errors have occurred in the past (Cole, 2005). It is up to a human examiner to work through this list, comparing the overall pattern and flow of the prints as well as the fine details in each, such as ridge endings, bifurcations, contours, islands, dots, breaks, creases, pores, and enclosures. The algorithm then returns a list of potential candidates ranked from the most to the least similar. When a fingerprint is recovered from a crime scene, a computer algorithm is used to compare the print to tens of millions of prints stored in a database. Integrating collective intelligence processes into existing forensic identification and verification systems could play a significant role-alongside effective training methods and evidence-based practices-in developing reliable and resilient systems to ensure the rule of law is justly applied. Our results show that pooling the decisions of small, independent groups of examiners can substantially boost the overall performance of these crowds and reduce the influence of errors. In this experiment, we examine one such countermeasure, which exploits the collective intelligence of groups of professional fingerprint analysts. The aim, then, is to build safeguards into these systems that mitigate the impact of these mistakes in practice. These mistakes are unavoidable, even in other high stakes, safety-critical domains such as medicine, aviation, or nuclear power. In response to these criticisms, a number of experiments have now been conducted, demonstrating that professional fingerprint analysts are impressively accurate compared to novices when distinguishing between crime-scene prints from the same and different sources-but they still make mistakes. Several reports by peak scientific and regulatory bodies have been roundly critical of the dearth of evidence supporting traditional forensic methods and practices such as fingerprint analysis. Our results show that combining independent judgements from small groups of fingerprint analysts can improve their performance and prevent these mistakes from entering courts. Aggregating people’s judgements by selecting the majority decision performs better than selecting the decision of the most confident or the most experienced rater. Pooling the decisions of novices results in a similar drop in false negatives, but increases their false-positive rate by up to 11%. When we pool the decisions of small groups of experts by selecting the decision of the majority, however, their false-positive rate decreases by up to 8% and their false-negative rate decreases by up to 12%. ![]() We replicate the previous findings that individual experts greatly outperform individual novices, particularly in their false-positive rate, but they do make mistakes. Here, we extend this “wisdom of crowds” approach to fingerprint analysis by comparing the performance of individuals to crowds of professional analysts. ![]() This redundancy in the system allows it to continue operating effectively even in the face of rare and random errors. One method to offset mistakes in these safety-critical domains is to distribute these important decisions to groups of raters who independently assess the same information. Several experiments have now shown that these professional analysts are highly accurate, but not infallible, much like other fields that involve high-stakes decision-making. ![]() When a fingerprint is located at a crime scene, a human examiner is counted upon to manually compare this print to those stored in a database.
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