“Cannot Decide”: The Fine Line Between Appropriate Inconclusive Determinations Versus Unjustifiably Deciding Not To Decide†
Corresponding Author
Itiel E. Dror Ph.D.
University College London, London, UK
Corresponding author: Itiel E. Dror, Ph.D. E-mail: [email protected]Search for more papers by this authorGlenn Langenburg Ph.D.
Elite Forensic Services, LLC, Saint Paul, MN
Search for more papers by this authorCorresponding Author
Itiel E. Dror Ph.D.
University College London, London, UK
Corresponding author: Itiel E. Dror, Ph.D. E-mail: [email protected]Search for more papers by this authorGlenn Langenburg Ph.D.
Elite Forensic Services, LLC, Saint Paul, MN
Search for more papers by this authorSee Comment here
Abstract
Inconclusive decisions, deciding not to decide, are decisions. We present a cognitive model which takes into account that decisions are an outcome of interactions and intersections between the actual data and human cognition. Using this model it is suggested under which circumstances inconclusive decisions are justified and even warranted (reflecting proper caution and meta-cognitive abilities in recognizing limited abilities), and, conversely, under what circumstances inconclusive decisions are unjustifiable and should not be permitted. The model further explores the limitations and problems in using categorical decision-making when the data are actually a continuum. Solutions are suggested within the forensic fingerprinting domain, but they can be applied to other forensic domains, and, with modifications, may also be applied to other expert domains.
References
- 1Sartre J. Being and nothingness: a phenomenological essay on ontology. Translated by Hazel E. Barnes. New York, NY: Washington Square Press, 1956; 55–6.
- 2Ulery BT, Hicklin AR, Roberts MA, Buscagliac J. Factors associated with latent fingerprint exclusion determinations. Forensic Sci Int 2017; 275: 65–75.
- 3Plous S. The psychology of judgment and decision-making. New York, NY: McGraw-Hill, 1993.
- 4Penner T. Socrates and the early dialogues. In: R Kraut, editor. The Cambridge companion to Plato. Cambridge, UK: Cambridge University Press, 1992; 121–69.
10.1017/CCOL0521430186.004 Google Scholar
- 5Lina SW, Bier VM. A study of expert overconfidence. Reliab Eng Syst Safe 2008; 93(5): 711–21.
- 6DePaulo BM, Charlton K, Cooper H, Lindsay JJ, Muhlenbruck L. The accuracy-confidence correlation in the detection of deception. Pers Soc Psychol Rev 1997; 1(4): 346–57.
- 7 S Harnad, editor. Categorical perception: the groundwork of cognition. New York, NY: Cambridge University Press, 1987.
- 8Bornstein MH, Korda NO. Discrimination and matching within and between hues measured by reaction times: some implications for categorical perception and levels of processing. Psychol Res 1984; 46(3): 207–22.
- 9Roberson D, Davidoff J. The categorical perception of colors and facial expressions: the effect of verbal interference. Mem Cognit 2000; 28: 977–86.
- 10Biederman I, Shiffrar MM. Sexing day-old chicks: a case study and expert systems analysis of a difficult perceptual-learning task. J Exp Psychol Learn Mem Cogn 1987; 13(4): 640–5.
- 11Evett IW, Williams RL. A review of the sixteen points fingerprint standard in England and Wales. Fingerprint Whorld 1995; 21(82): 125–43.
- 12Dror IE. Biases in forensic experts. Science 2018; 360(6386): 243 DOI: 10.1126/science.aat8443.
- 13Dror IE. Cognitive neuroscience in forensic science: understanding and utilising the human element. Philos Trans R Soc Lond B Biol Sci 2015; 370(1674): 20140255.
- 14Jeanguenat AM, Dror IE. Human factors effecting forensic decision making: workplace stress and wellbeing. J Forensic Sci 2018; 63(1): 258–61.
- 15Busemeyer JR, Townsend JT. Decision field theory: a dynamic-cognitive approach to decision-making in an uncertain environment. Psychol Rev 1993; 100(3): 4323–459.
- 16Dror IE, Busemeyer JR, Basola B. Decision-making under time pressure: an independent test of sequential sampling models. Mem Cognit 1999; 27(4): 713–25.
- 17Gonzalez C, Fakhari P, Busemeyer J. Dynamic decision-making: learning processes and new research directions. Hum Factors 2017; 59(5): 713–21.
- 18Biedermann A, Bozza S, Taroni F. Decision theoretic properties of forensic identification: underlying logic and argumentative implications. Forensic Sci Int 2008; 177(2–3): 120–32.
- 19 Scientific Working Group on Friction Ridge Analysis Study and Technology (SWGFAST). Document #15 standard for the definition and measurement of rates of errors and non-consensus decisions in friction ridge examination; 11/24/2012 ver 2.0, 2012; http://www.swgfast.org/Documents.html (accessed June 8, 2018).
- 20Ulery BT, Hicklin RA, Buscaglia J, Roberts MA. Accuracy and reliability of forensic latent fingerprint decisions. Proc Natl Acad Sci USA 2011; 108(19): 7733–8.
- 21Langenburg G, Champod C, Genessay T. Informing the judgments of fingerprint analysts using quality metric and statistical assessment tools. Forensic Sci Int 2012; 219(1–3): 183–98.
- 22Langenburg G, Hall C, Rosemarie Q. Utilizing AFIS searching tools to reduce errors in fingerprint casework. Forensic Sci Int 2015; 257: 123–33.
- 23Dror IE. A hierarchy of expert performance. J Appl Res Mem Cogn 2016; 5(2): 121–7.
- 24Ulery BT, Hicklin RA, Buscaglia J, Roberts MA. Repeatability and reproducibility of decisions by latent fingerprint examiners. PLoS ONE 2012; 7(3): e32800.
- 25Champod C, Evett IW. A probabilistic approach to fingerprint evidence. J Forensic Identif 2001; 51(2): 101–22.
- 26Neumann C, Evett IW, Skerrett J. Quantifying the weight of evidence from a forensic fingerprint comparison: a new paradigm. J Royal Stat Soc 2012; 175(2): 371–415.
10.1111/j.1467-985X.2011.01027.x Google Scholar
- 27Egli NM, Champod C, Margot P. Evidence evaluation in fingerprint comparison and automated fingerprint identification systems – modeling within finger variability. Forensic Sci Int 2007; 167(2–3): 189–95.
- 28Abraham J, Champod C, Lennard C, Roux C. Spatial analysis of corresponding fingerprint features from match and close non-match populations. Forensic Sci Int 2013; 230(1–3): 87–98.
- 29Leegwater AJ, Meuwly D, Sjerps M, Vergeer P, Alberink I. Performance study of a score-based likelihood ratio system for forensic fingermark comparison. J Forensic Sci 2017; 62(3): 626–40.
- 30Swofford HJ, Koertner AJ, Zemp F, Ausdemore M, Liu A, Salyards MJ. A method for the statistical interpretation of friction ridge skin impression evidence: method development and validation. Forensic Sci Int 2018; 287: 113–26.
- 31Hicklin RA, Buscaglia J, Roberts MA. Assessing the clarity of friction ridge impressions. Forensic Sci Int 2013; 226(1–3): 106–17.
- 32Earwaker H, Morgan RM, Harris AJ, Hall LA. Fingermark submission decision-making within a UK fingerprint laboratory: do experts get the marks that they need? Sci Justice 2015; 55(4): 239–47.
- 33Dror IE, Thompson WC, Meissner CA, Kornfield I, Krane D, Saks M, et al. Context management toolbox: a linear sequential unmasking (LSU) approach for minimizing cognitive bias in forensic decision making. J Forensic Sci 2015; 60(4): 1111–2.
- 34 ASTM. Standard E 1658-04: standard terminology for expressing conclusions of forensic document examiners. West Conshohocken, PA: ASTM, 2004.
- 35 Scientific Working Group on Friction Ridge Analysis Study and Technology (SWGFAST). Document #10 standards for examining friction ridge impressions and resulting conclusions, 04/27/13 ver. 2.1 Draft for Comment, 2013; http://www.swgfast.org/Documents.html (accessed June 8, 2018).
- 36Evett IW, Jackson G, Lambert JA, McCrossan S. The impact of the principles of evidence interpretation on the structure and content of statements. Sci Justice 2000; 40(4): 233–9.
- 37Thompson WC, Grady RH, Lai E, Stern HS. Perceived strength of forensic scientists’ reporting statements about source conclusions. Law Probab Risk 2018. https://doi.org/10.1093/lpr/mgy012. Epub 2018 June 1.
- 38 Office of the Inspector General (OIG). A review of the FBI's progress in responding to the recommendations in the Office of the Inspector General Report on the fingerprint misidentification in the Brandon Mayfield case. Washington, DC: Office of the Inspector General (OIG), 2011.
- 39Dror IE, Champod C, Langenburg G, Charlton D, Hunt H, Rosenthal R. Cognitive issues in fingerprint analysis: inter- and intra-expert consistency and the effect of a ‘target’ comparison. Forensic Sci Int 2011; 208(1–3): 10–7.
- 40Ulery BT, Hicklin RA, Roberts MA, Buscaglia J. Interexaminer variation of minutia markup on latent fingerprints. Forensic Sci Int 2016; 264: 89–99.
- 41 National Commission on Forensic Science. Ensuring that forensic analysis is based upon task-relevant information, 2015; https://www.justice.gov/ncfs/file/818196/download (accessed June 8, 2018).
- 42 Forensic Science Regulator. Guidance: cognitive bias effects relevant to forensic science examinations. FSR-G-217, 2015; https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/510147/217_FSR-G-217_Cognitive_bias_appendix.pdf (accessed June 8, 2018).