摘要: A range of forensic identification technologies have been introduced to assist examiners search through the large amounts data with which they are faced. These based on assumption that a human–technology partnership will be more effective than either human or technology alone. However, performance in field comparison is highly variable and humans by their very nature susceptible bias. Contrary depictions popular media, not perfect either. While accurate improvements consistently being realized, error variables can impact accuracy. This article outlines how technical realities interact an examiner's own biases influence decision outcomes. It also demonstrates making biased way information presented technology, potentially lead erroneous decisions made. The concludes discussion increasing awareness, practical training, adjustments workflow, interface design sensitive cognitive capabilities users help minimize such biases. Keywords: forensic technology; automated fingerprint system; AFIS; facial recognition system; DNA database; voice recognition; biometrics; bias; cognitive bias; technology
sci-hub.se 参考文章(29)
David White, A. Mike Burton, Richard I. Kemp, Rob Jenkins, Crowd Effects in Unfamiliar Face Matching Applied Cognitive Psychology. ,vol. 27, pp. 769- 777 ,(2013) , 10.1002/ACP.2971
Hunter A. Mcallister, Heather A. Stewart, James Loveland, Effects of Mug Book Size and Computerized Pruning on the Usefulness of Dynamic Mug Book Procedures Psychology Crime & Law. ,vol. 9, pp. 265- 277 ,(2003) , 10.1080/1068316031000081363
Stefan Röttger, Krisztina Bali, Dietrich Manzey, Impact of automated decision aids on performance, operator behaviour and workload in a simulated supervisory control task Ergonomics. ,vol. 52, pp. 512- 523 ,(2009) , 10.1080/00140130802379129
Rebecca Heyer, Carolyn Semmler, Forensic confirmation bias: the case of facial image comparison Journal of applied research in memory and cognition. ,vol. 2, pp. 68- 70 ,(2013) , 10.1016/J.JARMAC.2013.01.008
Markus Bindemann, Meri Avetisyan, Tim Rakow, Who can recognize unfamiliar faces? Individual differences and observer consistency in person identification. Journal of Experimental Psychology: Applied. ,vol. 18, pp. 277- 291 ,(2012) , 10.1037/A0029635
Raja Parasuraman, Dietrich H. Manzey, Complacency and bias in human use of automation: an attentional integration. Human Factors. ,vol. 52, pp. 381- 410 ,(2010) , 10.1177/0018720810376055
I. E. Dror, S. A. Cole, The vision in “blind” justice: Expert perception, judgment, and visual cognition in forensic pattern recognition Psychonomic Bulletin & Review. ,vol. 17, pp. 161- 167 ,(2010) , 10.3758/PBR.17.2.161
Ahmed M. Megreya, Markus Bindemann, Catriona Havard, A. Mike Burton, Identity-Lineup Location Influences Target Selection: Evidence from Eye Movements Journal of Police and Criminal Psychology. ,vol. 27, pp. 167- 178 ,(2012) , 10.1007/S11896-011-9098-7
James T. Townsend, Georgie Nozawa, Spatio-temporal properties of elementary perception: an investigation of parallel, serial, and coactive theories Journal of Mathematical Psychology. ,vol. 39, pp. 321- 359 ,(1995) , 10.1006/JMPS.1995.1033
Mary T. Dzindolet, Linda G. Pierce, Hall P. Beck, Lloyd A. Dawe, The perceived utility of human and automated aids in a visual detection task. Human Factors. ,vol. 44, pp. 79- 94 ,(2002) , 10.1518/0018720024494856