Collective intelligence in fingerprint analysis
Abstract 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. Several experiments have now shown that these professional analysts are highly accurate, but not infallible, much like other fields that involve hig...
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doaj-c350757e759a415391f5e3b39a571b402020-11-25T03:33:18ZengSpringerOpenCognitive Research2365-74642020-05-01511710.1186/s41235-020-00223-8Collective intelligence in fingerprint analysisJason M. Tangen0Kirsty M. Kent1Rachel A. Searston2School of Psychology, The University of QueenslandSchool of Psychology, The University of QueenslandSchool of Psychology, The University of AdelaideAbstract 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. 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. 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. This redundancy in the system allows it to continue operating effectively even in the face of rare and random errors. Here, we extend this “wisdom of crowds” approach to fingerprint analysis by comparing the performance of individuals to crowds of professional analysts. We replicate the previous findings that individual experts greatly outperform individual novices, particularly in their false-positive rate, but they do make mistakes. 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%. Pooling the decisions of novices results in a similar drop in false negatives, but increases their false-positive rate by up to 11%. Aggregating people’s judgements by selecting the majority decision performs better than selecting the decision of the most confident or the most experienced rater. Our results show that combining independent judgements from small groups of fingerprint analysts can improve their performance and prevent these mistakes from entering courts.http://link.springer.com/article/10.1186/s41235-020-00223-8Collective intelligenceWisdom of crowdsExpertiseFingerprintsForensic science |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jason M. Tangen Kirsty M. Kent Rachel A. Searston |
spellingShingle |
Jason M. Tangen Kirsty M. Kent Rachel A. Searston Collective intelligence in fingerprint analysis Cognitive Research Collective intelligence Wisdom of crowds Expertise Fingerprints Forensic science |
author_facet |
Jason M. Tangen Kirsty M. Kent Rachel A. Searston |
author_sort |
Jason M. Tangen |
title |
Collective intelligence in fingerprint analysis |
title_short |
Collective intelligence in fingerprint analysis |
title_full |
Collective intelligence in fingerprint analysis |
title_fullStr |
Collective intelligence in fingerprint analysis |
title_full_unstemmed |
Collective intelligence in fingerprint analysis |
title_sort |
collective intelligence in fingerprint analysis |
publisher |
SpringerOpen |
series |
Cognitive Research |
issn |
2365-7464 |
publishDate |
2020-05-01 |
description |
Abstract 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. 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. 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. This redundancy in the system allows it to continue operating effectively even in the face of rare and random errors. Here, we extend this “wisdom of crowds” approach to fingerprint analysis by comparing the performance of individuals to crowds of professional analysts. We replicate the previous findings that individual experts greatly outperform individual novices, particularly in their false-positive rate, but they do make mistakes. 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%. Pooling the decisions of novices results in a similar drop in false negatives, but increases their false-positive rate by up to 11%. Aggregating people’s judgements by selecting the majority decision performs better than selecting the decision of the most confident or the most experienced rater. Our results show that combining independent judgements from small groups of fingerprint analysts can improve their performance and prevent these mistakes from entering courts. |
topic |
Collective intelligence Wisdom of crowds Expertise Fingerprints Forensic science |
url |
http://link.springer.com/article/10.1186/s41235-020-00223-8 |
work_keys_str_mv |
AT jasonmtangen collectiveintelligenceinfingerprintanalysis AT kirstymkent collectiveintelligenceinfingerprintanalysis AT rachelasearston collectiveintelligenceinfingerprintanalysis |
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