Literature DB >> 33718855

Implementation of algorithms in pattern & impression evidence: A responsible and practical roadmap.

H Swofford1, C Champod1.   

Abstract

Over the years, scientific and legal scholars have called for the implementation of algorithms (e.g., statistical methods) in forensic science to provide an empirical foundation to experts' subjective conclusions. Despite the proliferation of numerous approaches, the practitioner community has been reluctant to apply them operationally. Reactions have ranged from passive skepticism to outright opposition, often in favor of traditional experience and expertise as a sufficient basis for conclusions. In this paper, we explore why practitioners are generally in opposition to algorithmic interventions and how their concerns might be overcome. We accomplish this by considering issues concerning human-algorithm interactions in both real world domains and laboratory studies as well as issues concerning the litigation of algorithms in the American legal system. Taking into account those issues, we propose a strategy for approaching the implementation of algorithms, and the different ways algorithms can be implemented, in a responsible and practical manner.
© 2021 The Author(s).

Entities:  

Keywords:  Algorithms; Automation; Forensic science; Models; Pattern evidence; Statistics

Year:  2021        PMID: 33718855      PMCID: PMC7933265          DOI: 10.1016/j.fsisyn.2021.100142

Source DB:  PubMed          Journal:  Forensic Sci Int        ISSN: 2589-871X            Impact factor:   2.395


  31 in total

Review 1.  Clinical versus actuarial judgment.

Authors:  R M Dawes; D Faust; P E Meehl
Journal:  Science       Date:  1989-03-31       Impact factor: 47.728

2.  Error Rates, Likelihood Ratios, and Jury Evaluation of Forensic Evidence.

Authors:  Brandon L Garrett; William E Crozier; Rebecca Grady
Journal:  J Forensic Sci       Date:  2020-04-22       Impact factor: 1.832

3.  Computation of likelihood ratios in fingerprint identification for configurations of three minutiae.

Authors:  Cedric Neumann; Christophe Champod; Roberto Puch-Solis; Nicole Egli; Alexandre Anthonioz; Didier Meuwly; Andie Bromage-Griffiths
Journal:  J Forensic Sci       Date:  2006-11       Impact factor: 1.832

4.  Fingermark evidence evaluation based on automated fingerprint identification system matching scores: the effect of different types of conditioning on likelihood ratios.

Authors:  Ivo Alberink; Arent de Jongh; Crystal Rodriguez
Journal:  J Forensic Sci       Date:  2013-11-01       Impact factor: 1.832

Review 5.  Why we still use our heads instead of formulas: toward an integrative approach.

Authors:  B Kleinmuntz
Journal:  Psychol Bull       Date:  1990-05       Impact factor: 17.737

6.  Quantifying the weight of fingerprint evidence through the spatial relationship, directions and types of minutiae observed on fingermarks.

Authors:  Cedric Neumann; Christophe Champod; Mina Yoo; Thibault Genessay; Glenn Langenburg
Journal:  Forensic Sci Int       Date:  2015-01-16       Impact factor: 2.395

7.  Spatial analysis of corresponding fingerprint features from match and close non-match populations.

Authors:  Joshua Abraham; Christophe Champod; Chris Lennard; Claude Roux
Journal:  Forensic Sci Int       Date:  2012-11-13       Impact factor: 2.395

8.  A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation.

Authors:  Didier Meuwly; Daniel Ramos; Rudolf Haraksim
Journal:  Forensic Sci Int       Date:  2016-04-26       Impact factor: 2.395

9.  A method for the statistical interpretation of friction ridge skin impression evidence: Method development and validation.

Authors:  H J Swofford; A J Koertner; F Zemp; M Ausdemore; A Liu; M J Salyards
Journal:  Forensic Sci Int       Date:  2018-04-03       Impact factor: 2.395

10.  Forensic comparison and matching of fingerprints: using quantitative image measures for estimating error rates through understanding and predicting difficulty.

Authors:  Philip J Kellman; Jennifer L Mnookin; Gennady Erlikhman; Patrick Garrigan; Tandra Ghose; Everett Mettler; David Charlton; Itiel E Dror
Journal:  PLoS One       Date:  2014-05-02       Impact factor: 3.240

View more
  4 in total

1.  Probabilistic reporting and algorithms in forensic science: Stakeholder perspectives within the American criminal justice system.

Authors:  H Swofford; C Champod
Journal:  Forensic Sci Int Synerg       Date:  2022-02-12

2.  The opacity myth: A response to Swofford & Champod (2022).

Authors:  Geoffrey Stewart Morrison; Nabanita Basu; Ewald Enzinger; Philip Weber
Journal:  Forensic Sci Int Synerg       Date:  2022-06-19

Review 3.  Advancing a paradigm shift in evaluation of forensic evidence: The rise of forensic data science.

Authors:  Geoffrey Stewart Morrison
Journal:  Forensic Sci Int Synerg       Date:  2022-05-18

4.  Machine learning algorithms in forensic science: A response to Morrison et al. (2022).

Authors:  H Swofford; C Champod
Journal:  Forensic Sci Int Synerg       Date:  2022-08-05
  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.