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The evaluation of the joint value of paint and toolmark evidence using Bayesian networks

The evaluation of the joint value of paint and toolmark evidence using Bayesian networks. 2018 Impression Pattern and Trace Evidence Symposium Arlington, VA Jan 25, 2018 Patrick Buzzini 1 & Nicholas D.K. Petraco 2.

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The evaluation of the joint value of paint and toolmark evidence using Bayesian networks

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  1. The evaluation of the joint value of paint and toolmark evidence using Bayesian networks 2018 Impression Pattern and Trace Evidence Symposium Arlington, VA Jan 25, 2018 Patrick Buzzini1 & Nicholas D.K. Petraco2 • Department of Forensic Science, Sam Houston State University, Huntsville, TX • Department of Sciences, John Jay College of Criminal Justice, City University of New York, New York, NY

  2. Interactions of interest Problem #1: Imperfect record Quality and quantity of marks or debris may be limited

  3. Observations of features Acquired feature(s) Architectural paint Tool paint • Manufacturing feature(s): • Shape; • Width. PBuzzini, UNIL, 2000

  4. Interactions of interest Problem #2: more than one type of evidence may be part of a single activity of interest. • Outcomes of the two evidence types offered separately. • Who is supposed to evaluate the simultaneous contributions of more than one evidence type? • Example of scenario is given where, • Transfer of a toolmark at the scene is not disputed; • The source of a toolmarkat the scene is uncertain; • Transfer of tool paint at the scene is not disputed; • The source of tool paint at the scene uncertain; • Transfer of architectural paint on the seized tool is uncertain; • The source of architectural paint on the seized tool is uncertain.

  5. Hierarchy of propositions O. The suspect committed the breaking-and-entering? Or someone else did? Offence level Other evidence The seized tool was used to force the door window? Or another tool was used? Activity level S1. Does the toolmark originate from the seized tool? Orfrom another tool? S2. Do the paint debris at the scene come from the seized tool? Or from another tool? S3. Do the paint debris on the tool come from the door window frame? Or from another painted object? Source level Concept developed by: Cook R, Evett I. W., Jackson G., Jones P.J., Lambert J. A. A Hierarchy of Propositions: Deciding Which Level to Address in Casework. Science & Justice 1998: 38(4): 231-239.

  6. Bayesian networks • Graphical models that provide a means to: • organize a complex problem with different variables; • Recognize dependencies between variables; • make inferences according to the laws of probability. • BNs are composed of: • nodesthat represent random variables (discrete or continuous); • arrows(causal arrows) that highlight dependencies and denote the probabilistic relationships between these nodes. Diverging connection Serial connection Converging connection Source: Taroni F, Biedermann A, Bozza S, Garbolino P, Aitken C. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science (2nded). Wiley & Sons, Chichester, UK (2014)

  7. BN construction The presence of paint as a result of a transfer in a direction informs about the presence of paint as a result of a transfer in the opposite direction. Aitken C, Taroni F, Garbolino P. A graphical model for the evaluation of cross-transfer evidence in DNA profiles. Theoretical Population Biology 2003; 63: 179-190. Champod C, Taroni F. The Bayesian approach (ch. 13.3). In: Robertson J, Grieve M (eds.). Forensic Examination of Fibres(2nd ed.). CRC Press, Boca Raton, FL (1999): 379-398. TaroniF, Biedermann A, Bozza S, Garbolino P, Aitken C. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science (2nded). Wiley & Sons, Chichester, UK (2014).

  8. Conditional Probability Tables Full width present: 3.1cm => 10/198 occurrences (~0.05) Partial toolmark present: width at least 3.1cm => 159/198 occurrences (~0.80) Source: Buzzini P, Massonnet G, Birrer S, Egli N, Mazzella W, Fortini A. Survey of Crowbar and Household Paints in Burglary Cases: Population Study, Transfer and Interpretation. Forensic Science International 2005;152(2-3): 221-234.

  9. Conditional Probability Tables Source: PetracoNDK, Berry R, Del Valle A, Gambino C, Kammrath BW, SpeirJ, ShenkinP. (2017). Specific Pattern Identification Uncertainty Via Computer Matching Systems. Law Probability and Risk, ICFIS 17 Proceedings (submitted).

  10. 0 Number of simulations 50 b a ub|a Conditional Probability Tables u0|00; u | 0 0; u0| 0 0 none none ufew| few 38/40 0.95 38 few 40 few 2 ulots| few 2/40 0.05 lots 7 ufew|lots 7/10 0.7 few 10 lots 3 ulots|lots 3/10  0.3 lots Source: Buzzini P, Massonnet G, Birrer S, Egli N, Mazzella W, Fortini A. Survey of Crowbar and Household Paints in Burglary Cases: Population Study, Transfer and Interpretation. Forensic Science International 2005;152(2-3): 221-234.

  11. Conditional Probability Tables Source: Buzzini P, Massonnet G, Birrer S, Egli N, Mazzella W, Fortini A. Survey of Crowbar and Household Paints in Burglary Cases: Population Study, Transfer and Interpretation. Forensic Science International 2005;152(2-3): 221-234.

  12. Conditional Probability Tables f2 ≅ 0.22 Evett I. A quantitative theory for interpreting transfer evidnece in criminal cases. Applied Statistics 1984; 33: 25-32. Champod C, Taroni F. The Bayesian approach (ch. 13.3). In: Robertson J, Grieve M (eds.). Forensic Examination of Fibres(2nd ed.). CRC Press, Boca Raton, FL (1999): 379-398. Unpublished work Source: Buzzini P, Massonnet G, Birrer S, Egli N, Mazzella W, Fortini A. Survey of Crowbar and Household Paints in Burglary Cases: Population Study, Transfer and Interpretation. Forensic Science International 2005;152(2-3): 221-234.

  13. Toolmark evidence evaluation 1. Correspondence of manufacturing features of a partial toolmark only. 2. Correspondence of both manufacturing and acquired features of the toolmark.

  14. Paint evidence evaluation The paint debris at the scene are differentiated from the reference paint of the suspected tool. The paint debris at the scene NOT differentiated from the reference paint of the suspected tool. And, The paint debris on the tool NOT differentiated from the reference paint of the damaged property.

  15. Toolmark and paint evidence joint evaluation

  16. Discussion • Possibility to consider multiple variables simultaneously and how they affect the question of interest individually and collectively. • Expert judgment: different values by different experts. • BN can be populated with values of two opposing experts to see the impact on such divergence; • Divergences may be impactless in the light of the overall assessment. • “We don’t do that because we do not have data!” • First a framework for evidence interpretation needs to be defined, then data can be obtained; • “We can’t quantify, so we can’t do!”

  17. Discussion PBuzzini, UNIL, 2000

  18. Conclusion • “Big picture” view of physical evidence outcomes with regards to questions of interest; • Possibility to predict the impact of outcomes prior to a laboratory examination. • Data from observations may not be ideal. => Will they ever be? • Who is responsible to interpret the joint value of various types of scientific evidence?

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