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                            Library & Multimedia
 Quantification of the weight of fingerprint evidence using a ROC-based Approximate Bayesian Computation algorithm for model selection
NCJ Number
              304328

Date Published

March 2022
Annotation
              This article reports on the use of an Approximate Bayesian Computation (ABC) model selection algorithm to quantify the weight of fingerprint evidence.
Abstract
              For more than a century, fingerprints have been used with considerable success to identify criminals or verify the identity of individuals. The categorical conclusion scheme used by fingerprint examiners, and more generally the inference process followed by forensic scientists, have been heavily criticized in the scientific and legal literature. Instead, scholars have proposed to characterize the weight of forensic evidence using the Bayes factor as the key element of the inference process. In forensic science, quantifying the magnitude of support is equally as important as determining which model is supported. Unfortunately, the complexity of fingerprint patterns makes likelihood-based inference impossible. The current project supplemented the original ABC model selection algorithm using a Receiver Operating Characteristic curve to mitigate its known shortcomings with respect to the choice of a suitable threshold and the curse of dimensionality. In a sense, this offers an alternative to other methods that have tried to address the same issues. This method is straightforward to implement, computationally efficient, and visually intuitive for lay individuals (i.e., jurors). This method was used to quantify the weight of fingerprint evidence in forensic science, but it can be applied to any other forensic pattern evidence. (Publisher Abstract)
      
  
          Additional Details
Grant Number(s)
        2014-IJ-CX-K088
                                2015-DN-BX-K027


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)


  
  
        
  Award
        Foundational Research into the Quantification of the Value of Forensic Evidence for Complex Evidential Forms arising from Impression and Pattern Evidence
                                Benefits and Limitations of the Statistical Methodology in Determining Evidentiary Value of Latent Print Identification: A Prospective Evaluation
    
  
  
Date Published: March 10, 2022
    
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      PatentsRelated Topics
        
                Forensic sciences
        Evidence analysis and processing
        Fingerprint analysis
        Fingerprints
        Probabilistic evidence



  
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