Interviewing and deception detection techniques for rapid screening and credibility assessment
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Interviewing and Deception Detection Techniques for Rapid Screening and Credibility Assessment. Dr. Jay F. Nunamaker, Jr. Dr. Judee K. Burgoon. Agenda. Introduction Project Phases Project Plan Year One: Unique Datasets Year One: Sensors and Tools Year One: Tasks

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Interviewing and Deception Detection Techniques for Rapid Screening and Credibility Assessment

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Interviewing and deception detection techniques for rapid screening and credibility assessment

Interviewing and Deception Detection Techniques for Rapid Screening and Credibility Assessment

Dr. Jay F. Nunamaker, Jr.

Dr. Judee K. Burgoon


Agenda

Agenda

  • Introduction

  • Project Phases

  • Project Plan

    • Year One: Unique Datasets

    • Year One: Sensors and Tools

    • Year One: Tasks

      • Analysis Psycho-Physiological Datasets

      • Lexical Analysis

      • Interoperable Video Database

      • Collaborative Credibility Assessment Tools

  • Conclusion


Introduction

Introduction

  • Identify verbal/non-verbal behaviors and physiological cues that indicate deception and hostile intentions in rapid screening environments

  • Experimental research to evaluate and develop automated deception detection technology

  • Develop questioning and information elicitation strategies for border screeners


Project phases

Project Phases

  • Experimentation and analysis of credibility assessment tools

    • Test new deception detection technology which incorporate verbal/non-verbal behavior and physiological measures

    • Prototype automated systems and enabling technologies for detecting deception

    • Replicate knowledge learned in experiments in the field

    • Analysis of interviewing techniques in screening scenarios

    • Techniques for information elicitation

    • Behavioral analysis and questioning strategies

  • Screening and border specific analysis of detection methods

    • Unobtrusive methods for behavior monitoring and deception detection

    • Interview and screening techniques


Project plan

Project Plan


Project plan year 1 unique d atasets

Project Plan – Year 1Unique Datasets

  • Datasets for original analysis:

    • Cultural Benchmarks

      • 220 international participants

      • Professionally interviewed (25 questions)

      • Lie or truth instructions

    • Mock Crime

      • 134 participants

      • Realistic Mock theft

        scenario

  • New Proposed Experiments


Project plan year 1 mock crime experiment example

Project Plan – Year 1Mock Crime: Experiment Example

Stage 1: Subject arrival at separate building

Stage 2: Subject receives instructions by recording

Stage 4: Subject completes credibility interview about involvement in theft

Stage 3: Subject arrives at secretary’s office to steal ring


Project plan year 1 sensors and tools

Project Plan – Year 1Sensors and Tools

  • PUPILOMETRY

  • EYE-TRACKING

  • KINESIC

Equipment supplied by:


Project plan year 1 task 1 analysis psycho physiological datasets

Project Plan – Year 1 Task 1: Analysis Psycho-Physiological Datasets

  • Phase 1: LDV Data Analysis (Year 1)

    • Unintentionally leaked psycho-physiological cues may be indicative of deceptive behavior

    • “Cultural Benchmarks” experiment captured Pulse and Respiration data via LDV

    • Determine if the LDV can provide cues that are indicative of deception

  • Phase 2: Data from multi-sensors (Year 2)

    • Cultural Benchmarks Experiment – sensor data analyzed individually – not looked at collectively

      • Pulse Respiration, Kinesics (Blob, ASM, Gestures, Blinking, Pose), and Pupilometry

  • Phase 3: Data-fusion techniques (Year 2)

    • How do we fuse data from distinct sources?

    • Do fusion techniques provide greater accuracy in detecting deception?


Milestones and deliverables

Milestones and Deliverables

*Year one deliverables in green


Project plan year 1 task 2 lexical analysis

Project Plan – Year 1Task 2: Lexical Analysis

  • “words whose job it is to make things more or less fuzzy” (Lakoff, 1972)

  • e.g. perhaps, might, maybe, approximately

  • Communicates speaker’s degree of confidence (Hyland, 1998; Coates, 1987)

  • Reduces strength of a statement (Zucker & Zucker, 1986)

  • Expresses tentativeness and probability

  • Theoretically linked to deception use


Milestones and deliverables1

Milestones and Deliverables

*Year one deliverables in green


Project plan year 1 task 3 interoperable video database

Project Plan – Year 1Task 3: Interoperable Video Database

Currently have many large, disparate data sources. Challenges in managing large and diverse data sets include:

  • Integrating the datasets efficiently

  • Querying integrated data sets intelligently

    • Example: Retrieve all data associated w/specific gesture

  • Capturing tacit information

  • Determining the elementary/composite data elements

  • Creating semantic interoperability across the datasets

    In order to begin addressing these

    challenges, we need to begin to create a

    framework which will help us understand

    the datasets and how to manage

    them holistically.

Retrieve all data associated with shrugs

Cultural Benchmarks Interviews

Mock Crime Interviews


Milestones and deliverables2

Milestones and Deliverables

*Year one deliverables in green


Project plan year 1 task 4 collaborative credibility assessment tools

Project Plan – Year 1Task 4: Collaborative Credibility Assessment Tools

Problem: Individuals are not as accurate as machines in credibility assessment

Proposal: Use group collaboration to discover more cues to deception

First phase Deliverable: Determine feasibility of and requirements for collaboration tools for credibility assessment. Create prototype(s)


Milestones and deliverables3

Milestones and Deliverables

*Year one deliverables in green


Conclusion

Conclusion

  • Identify verbal/non-verbal behaviors and physiological cues that indicate deception and hostile intentions in rapid screening environments

  • Experimental research using unique datasets to evaluate and develop:

    • Automated deception detection technology

    • Experimental research to evaluate and develop automated deception detection technology

  • Four proposed tasks with deliverables in Year One


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