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Bayesian Networks for the Analysis Bayesian Networks for the Analysis of Evidence of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29 January 2007 A. Philip Dawid Amanda B. Hepler University College London

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Page 1: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Bayesian Networks for the Bayesian Networks for the Analysis of EvidenceAnalysis of Evidence

Graphic and Visual Representations of Evidence and Inference in Legal Settings

Cardozo School of Law29 January 2007

A. Philip Dawid Amanda B. Hepler

University College London

Page 2: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Wigmore Charts and Bayesian Networks

Object Oriented Bayesian Networks

Sacco and Vanzetti case

OutlineOutlineOutlineOutline

Page 3: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

U Harold S. unlawfully and intentionally assaulted and injured a security guard Willard R. during a break-in at the Blackbread Brewery premises, 27 Orchardson St., London NW8 in the early morning hours of 1 May 2003

Robbery CaseRobbery CaseRobbery CaseRobbery Case

Page 4: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

P1

U

P2 P3 P4

Wigmorean AnalysisWigmorean AnalysisWigmorean AnalysisWigmorean Analysis

Page 5: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

P1 In the early morning hours of 1 May, 2003, four men unlawfully broke into the premises of the Blackbread Brewery, located at 27 Orchardson St., London NW8

P2 Harold S. was one of the four men who broke into the premises of the Blackbread Brewery in the early morning hours of 1 May 2003

P3 A security guard at the Blackbread Brewery, Willard R., was assaulted and injured during the break-in

P4 It was Harold S. who intentionally assaulted and injured Willard R. during the break-in

Wigmorean AnalysisWigmorean AnalysisWigmorean AnalysisWigmorean Analysis

Page 6: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

 29) The intruders' car left immediately at the first sound of the alarm leaving the intruders stranded.30) Willard R. testimony to 29).31) The intruders dispersed from the Blackbread Brewery premises on foot.32) Willard R. testimony to 31).33) The four intruders went their separate ways. 34) In a search of the area surrounding the Blackbread Brewery premises, police apprehended Harold S. trying to "hot

wire" a car in an alley about 1/4 mile from the Blackbread Brewery premises.35) DI Leary testimony to 34).36) A photo of Harold S. taken shortly after his apprehension to be shown at trial. 37) The photo shown at trial is the same one police took of Harold R. shortly after his arrest.38) The car Harold S. was trying to "hot wire" did not belong to him.39) Harold S. was one of the four intruders fleeing the Blackbread Brewery premises.40) During the police investigation a short time after the intrusion, the police found a tuft of red fibres on a jagged end of

one of the cut edges of the metal grille on the Blackbread premises.41) DI Leary testimony to 40).42) The tuft of fibres to be shown at trial.43). The tuft of fibres shown at trial is the same one that police found on a jagged end of one of the cut edges of the metal

grille on the Blackbread premises.44) The tuft of the fibres found on the metal grille on the Blackbread Brewery premises is red acrylic.45) DI Leary testimony to 44). 46) The tuft of red acrylic fibres found on the metal grille came from an article of clothing.47) The article of clothing the fibres came from was being worn at the time of the break-in at the Blackbread Brewery. 48) Harold S. was wearing a jumper and jeans at the time of his apprehension.49) DI Leary testimony to 48).50) The jumper and jeans to be shown at trial. 51) The jumper and jeans to be shown at trial are the same ones the police took from Harold S. after his apprehension.

P2: Harold S. was one of the four men who broke into the premises of the Blackbread Brewery in the early morning hours of 1 May, 2003

Page 7: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

52) Harold S's jumper is made of red acrylic.53) DI Leary testimony to 52). 54) Harold S. was wearing this red acrylic jumper at the time of the break-in at Blackbread Brewery.55) The tuft of red fibres found on the metal grille on the Blackbread Brewery premises is visually indistinguishable from

the fibres on Harold S's jumper.56) DI Leary testimony to 55)57) The tuft of fibres and the jumper to be shown together at trial.58) The tuft of fibres and the jumper are the same ones police obtained during their investigation of the break-in at the

Blackbread Brewery.59) The tuft of red fibres found on the metal grille on the Blackbread Brewery premises is indistinguishable from the

fibres on Harold S's jumper as shown by a microspectroflourimetry analysis.60) DI Leary testimony.61) Microspectroflourimetry analysis result to be shown at trial.62) The microspectroflourimetry results shown at trial are the same ones police obtained from the forensic scientist

["boffin"] who performed the analysis.63) The tuft of red fibres found on the metal grille on the Blackbread Brewery premises is indistinguishable from the

fibres on Harold S's jumper as shown by a thin layer chromatography analysis.64) DI Leary testimony to 63).65) The results of the thin layer chromatography analysis.to be shown at trial.66) The thin layer chromatography results shown at trial are the same ones police obtained from the forensic scientist

who performed the analysis.67) The jumper belonging to Harold S. is well worn and has several holes in it.68) DI Leary testimony to 67.69) None of holes in Harold S's jumper can be clearly identified as a possible source of the tuft found on the metal grille

on the Blackbread Premises.70) DI Leary testimony to 69).71) Matches of tufts to holes in fabrics is very difficult.72) The jumper worn by Harold S. on 1 May, 2003 was torn on a hole in the metal grille at the Blackbread premises.73) Harold S. was wearing the article of clothing that produced the tuft of red acrylic found on a jagged end of the hole

cut into the metal grille at the Blackbread Brewery premises on 1 May, 2003.

74) Testimonial denial by Harold S. of P2, that he was one of four men who broke into the premises of the Blackbread Brewery in the early morning hours of 1 May, 2003.

Page 8: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

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P2

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P2: Harold S. was one of the four men who broke into the premises of the Blackbread Brewery in the early morning hours of 1 May, 2003

Wigmore ChartWigmore ChartWigmore ChartWigmore Chart

Page 9: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Bayesian NetworkBayesian NetworkBayesian NetworkBayesian Network

B

Y2

X2X1 RAX3

Y1

C G2W N G1

BLOOD EVIDENCE

FIBRE EVIDENCE

No. of offenders

Suspect’s blood type Guard’s blood typeJumper fibres

Whose fibres on grille?

Grille fibres

Whose blood on jumper?

Guard’s evidence of no. of offenders Suspect guilty?

Blood spray on jumper

Jumper blood type

Police evidence of arrest

EYE WITNESS EVIDENCE

Guard’s evidence of punch

Page 10: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Graphical inference networks used to model many items of evidence and their relationships

Represent individual standpoint rather than “objective truth”

Support coherent narrative and argumentation (?)

Commonalities of Wigmore Charts Commonalities of Wigmore Charts and Bayesian Networksand Bayesian Networks

Commonalities of Wigmore Charts Commonalities of Wigmore Charts and Bayesian Networksand Bayesian Networks

Page 11: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

• Tree-structured• Created for evidence

in hand• Nodes are events or

propositions• Arrows indicate

inferential flow • Qualitative analysis

and synthesis• Symbolic distinctions

of type/effect of evidence

DifferencesDifferencesDifferencesDifferences

Wigmore Chart Bayesian Network

• Directed Acyclic Graph• Created any time• Nodes are variables

(any number of states)

• Arrows indicate “causal” dependence

• Qualitative reasoning about relevance

• Structural distinctions of type/effect of evidence

Page 12: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Sacco and Vanzetti CaseSacco and Vanzetti CaseSacco and Vanzetti CaseSacco and Vanzetti Case

Page 13: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

U Sacco and Vanzetti were guilty of 1st degree murder in the slaying of Berardelli during the robbery that took place in South Braintree, MA on April 15, 1920

Sacco and Vanzetti CaseSacco and Vanzetti CaseSacco and Vanzetti CaseSacco and Vanzetti Case

Page 14: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

P1 Berardelli died of gunshot wounds he received on April 15, 1920.

P2 At the time he was shot, Berardelli, along with Parmenter, was in possession of a payroll.

P3 It was Sacco who, with the assistance of Vanzetti, intentionally fired shots that took the life of Berardelli during a robbery of the payroll he and Parmenter were carrying.

Sacco and Vanzetti CaseSacco and Vanzetti CaseSacco and Vanzetti CaseSacco and Vanzetti Case

Page 15: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Bayesian NetworkBayesian Network(Hugin 5)(Hugin 5)

Bayesian NetworkBayesian Network(Hugin 5)(Hugin 5)

P1

P2

P3

U

Page 16: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Large and messy

Complex modelling process

All evidence treated at same level

Hard to interpret

““Object-Oriented”Object-Oriented”Bayesian NetworkBayesian Network““Object-Oriented”Object-Oriented”Bayesian NetworkBayesian Network

Some undesirable featuresSome undesirable featuresSome undesirable featuresSome undesirable features

Page 17: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Sacco is the murderer?

1st Degree Murder?

Berardelli Murdered?

Felony Committed?

Medical evidence

Payroll robbery evidence

Level 1: 1Level 1: 1stst Degree Murder? Degree Murder?Level 1: 1Level 1: 1stst Degree Murder? Degree Murder?

P1 P2

P3

Page 18: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Sacco is the Murderer?

Consciousness of Guilt?

Firearms?Opportunity?

Eyewitnesses

Cap

Murder Car

Alibi

Motive?

Level 2: Sacco is the Murderer?Level 2: Sacco is the Murderer?Level 2: Sacco is the Murderer?Level 2: Sacco is the Murderer?

P3

Page 19: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Sacco at Scene?

Sacco’s Cap at Scene?

Alibi?Eyewitnesses?

Pelser Constantino

Wade

Murder Car?

Level 3: OpportunityLevel 3: OpportunityLevel 3: OpportunityLevel 3: Opportunity

Page 20: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Level 4: Eyewitness TestimonyLevel 4: Eyewitness Testimony

Similar to Sacco?

Pelser’s Credibility

Pelser’s Testimony

Wade’s Credibility

Wade’s Testimony

Sacco at Scene?

HUGIN 6HUGIN 6

Page 21: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Level 4: Eyewitness TestimonyLevel 4: Eyewitness Testimony

Eyewitnesses

HUGIN 6HUGIN 6

Page 22: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Level 5: Generic CredibilityLevel 5: Generic Credibility

Eyewitnesses

Generic Credibility

Testimony

Competent?

Veracity?

Objectivity?

Sensation?

Event

HUGIN 6HUGIN 6

Page 23: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Level 6: Attributes of CredibilityLevel 6: Attributes of Credibility

Eyewitnesses

Generic Credibility

Testimony

Competent?

Veracity?

Objectivity?

Sensation?

Event

Competent?

Sensation

Agreement?

Event

Sensation

HUGIN 6HUGIN 6

Page 24: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Level 6: Attributes of CredibilityLevel 6: Attributes of Credibility

Eyewitnesses

Generic Credibility

Testimony

Competent?

Veracity?

Objectivity?

Sensation?

Event Sensation

Noisy Channel

Out

In Error?

Competent?

Sensation

Agreement?

Event

HUGIN 6HUGIN 6

Page 25: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Level 4: Eyewitness TestimonyLevel 4: Eyewitness Testimony

Similar to Sacco?

Pelser’s Credibility

Pelser’s Testimony

Wade’s Credibility

Wade’s Testimony

Sacco at Scene?

HUGIN 6HUGIN 6

Page 26: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Level 4: Eyewitness TestimonyLevel 4: Eyewitness Testimony

Eyewitnesses

HUGIN 6HUGIN 6

Page 27: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Level 5: Specific CredibilityLevel 5: Specific Credibility

Eyewitnesses

Testimony

Event

Generic Credibility

Competent?

Evidence undercut by ancillary evidence

Constantino’s Testimony

HUGIN 6HUGIN 6

Page 28: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

XParent-Child

Y

p2

Generalization(warrant)

p1

X

True False

YTrue p1 1-p2

False 1-p1 p2

Boolean Case

Y Probabilities

X

Statistical Evidence

Expert Evidence

Other Generic ModulesOther Generic ModulesOther Generic ModulesOther Generic Modules

Page 29: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Item 1 = Item 2?

Attribute 1 ... Attribute N

Item 1 = Item 2?

Testimony

Attribute (Item 1)

Testimony

Attribute(Item 2)

Identification

“linked” evidence

Page 30: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

2 or more sources giving corroborative/ contradictory statements about same event

Event

Credibility Credibility

Source 1 Source 2

Corroboration/ Contradiction

Page 31: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Testimony on 2 or more compatible/ incompatible events

Hypothesis

Credibility Credibility

Source 1 Source 2

Event 1 Event 2

Convergence/Conflict

Page 32: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Knowledge of Cause 1 lowers probability of Cause 2

Event

Cause 1 Cause 2

Explaining Away

Page 33: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Top-level display as Wigmore chartVariable depth of display Tailor generic class properties to specific instance

Represent “causal” strengthDetermine impact of evidence

Wish ListWish ListWish ListWish List

Page 34: Bayesian Networks for the Analysis of Evidence Graphic and Visual Representations of Evidence and Inference in Legal Settings Cardozo School of Law 29

Thank you!