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Raja Parasuraman George Mason University Thomas Sanquist Pacific Northwest National Laboratory

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### Raja ParasuramanGeorge Mason UniversityThomas SanquistPacific Northwest National Laboratory

### National Academy of Sciences PanelMaking the Nation Safer

### RPM Human Factors Problems

### How Can RPM Performance Be Enhanced?

### Example Using Step 1

### Step 2. Setting Warning System Parameters for High Posterior Probability or Positive Predictive Value (PPV)

### Example Using Step 2

### Design Criteria for Increasing the RPM Predictive Value of a True Threat

### Likelihood Displays as an Additional Approach

### RPM Likelihood Display Concept

A Bayesian Likelihood Display Approach to

Enhancing Detection of Illicit Radioactive

Materials at Border Crossings and Ports

IEEE Transactions on Systems, Man, and Cybernetics: Part C: Applications, in press

- 16 Technology Solutions offered
- ALL involved
- Human operators
- Human-machine interfaces
- Organizational changes
- Selection and training issues
- NO Human Factors analysis
- 1 Panelist: Don Norman
- 1 Presenter: Raja Parasuraman

- Human Factors Problem: Radiation Portal Monitors (RPMs)
- Have high false alarm rates and very low true threat detection rate
- Create excessive workload for customs personnel
- Lower operator trust and may create “cry wolf” effect
- Theoretical approach
- Bayesian theory
- Signal detection theory and ROC analysis
- Likelihood display concept
- Field studies at Canada-US border crossings

Radiation Portal Monitors (RPMs)

- Object (e.g. truck) passes through large panel of RPM
- Detectors measure (gamma) and (neutron) radiation
- Alarm results if radiation count exceeds threshold
- If alarm occurs, secondary confirming scan conducted
- Identification of alarm source
- Resolution and discharge

Naturally Occurring Sources of Radiation (NORMs)

- Ceramics
- Fertilizer
- Cat litter
- Some fruits and vegetables (e.g., bananas)
- Medical implants

Daily RPM Freight Activity at All Canada-US Border Crossings

- Truck False Alarm Rate: 0.5 - 2%
- Total Daily False Alarms in 2006: 468
- Typical Daily False Alarms at Single Crossing: 30-40

Source: North American Transportation Statistics Database, 2007

What is the Detection Sensitivity (d’) of RPMs?

- With a known radioactive test source, RPM yields an alarm 95% of the time
- Hit rate = 0.95 (International Atomic Energy Agency, 2002).
- When no radioactive source is present , RPM alarms 2% of the time
- False alarm rate = 0.02
- Detection sensitivity d = z[p(hit)) – z(p(false alarms)] = 3.72
- Good, but not great
- Improved radiation detectors currently under development

What is the Sensitivity (d’) of RPMs for Classification of True Threats?

- Detection performance refers to any radioactive source, including innocent radiation (NORMs)
- When an illicit radioactive source is present, RPM alarms 95% of the time
- Hit rate rate = 0.95
- When non-threatening radiation is present (NORM), RPM alarms 98% of the time
- False alarm rate = 0.98
- Threat classification sensitivity d’ = 0

How Can Classification Sensitivity Be Increased?

- Use more reliable algorithms for classification
- However, even if d’ can be made higher, the low base rate of true threats poses a problem
- Need to increase the posterior probability of a true threat given an alarm — positive predictive value (PPV) of alarm

Bayes’ Theorem

Easy form: Odds in favor of hypothesis = Prior odds x Likelihood ratio of data

H1 = Hypothesis

H2 = Alternative hypothesis

D = Data (Diagnostic information)

P(H1) = Prior probability of hypothesis 1

P(H2) = Prior probability of hypothesis 2

P(H1/D) = Posterior probability of hypothesis 1 given data

P(H1/D) = P(H1) x P(D/ H1)

P(H2/D) P(H2) P(D/ H2)

Reverend Thomas Bayes. (1763) "Essay Towards Solving a Problem in the Doctrine of Chances”. Philosophical Transactions of the Royal Society of London.

Underestimation of effect of base rates

- H1 = Breast cancer
- H2 = Normal
- D = Positive mammography test
- P(H1) = Population base rate of breast cancer = 1% = .01
- P(H2) = 1 - .01 = .99
- P(D/ H1) = Given cancer (H1), positive test (D) occurs 90% of the time
- How well do people estimate the probability of cancer given a positive test?

P(H2/D) P(H2) P(D/ H2)

Underestimation of effect of base rates- Prior odds = P(H1) / P(H2) = .1/.99 = .01 (1 in 100 chance)
- P(D/ H1) = 90% correct = .9
- P(D/ H2) = 1 - .9 = .1
- Likelihood ratio = P(D/ H1) / P(D/ H2) = .9/.1 = 9 (9 to 1 chance)
- Posterior odds of breast cancer = .01 x 9 = .09 or about a 1 in 10 chance
- Most people misestimate the odds as closer to 9 to 1 than to 1 in 10

- Nuclear smuggling incidents in Europe as monitored
- by the International Atomic Energy Agency
- 450 attempts in 10 years = 45/year *
- 11,000,000 annual border crossings
- IAEA Base rate = 45/11,000,000 = 0.000041
- United Nations Non-Proliferation Committee estimate
- based on additional data on theft of weapons-grade plutonium from countries of former Soviet Union
- UNNPC Base rate = 0.0000076

IAEA

UNNPC

* Orlov, V.A. (2004). Illicit nuclear trafficking and the new agenda. IAEA Bulletin, 46:1, 53-56.

RPM Posterior Probability of True Alarm

- Posterior probability of a true threat given an alarm?
- IAEA estimate: Posterior probability = 0.0000039
- UNNPC estimate: Posterior probability = 0.00000073
- Alarm rate = 0.5 - 2% of traffic at Canada-US crossings
- True alarm: ONE every 2 to 6 years

P(threat/alarm) = P(alarm/threat)

P(alarm/threat) + P(alarm/no threat)([1-base rate]/base rate)

Workload in resolving and clearing freight false alarms

requires 30-45 minutes and two customs officers

Staffing shortages exacerbate the workload problem

Study conducted by Pacific Northwest National Lab at two Canada-US borders

N=12 customs personnel (8-hour work shift)

NASA-TLX workload and Trust (credibility) ratings of RPM at different periods during shift

Setting a minimum decision criterion

Setting a decision criterion that maximizes the posterior probability of a correct alarm response

The second step is usually ignored

in automated alerting system design

RECEIVER OPERATING CHARACTERISTIC (ROC)

1.0

ß = 0

.8

.6

Min. Decision

Threshold ßf

P(R|S)

.4

.2

ß = ∞

0

.2

.4

.6

.8

1.0

P(R|N)

Max. Permissible

False Alarm Rate f

Step 1. Setting Minimum Decision Thresholds for Warning Systems: [Necessary but Not Sufficient]

Requirement: For a warning system with d’ = 6, the false alarm rate should not exceed .001

Then the decision threshold for the system should be set such that ßf = 1.71 or higher

For ßf =1.71, only .18% of hazardous events will be missed, and only .1% of non-hazardous events will be responded to falsely

0.9

0.8

0.7

0.6

Increasing ß

0.5

Posterior Probability p(S|R)

0.4

0.3

0.2

0.1

0.0

0.00

0.02

A Priori Probability p or Base Rate

0.04

0.06

0.08

0.10

POSTERIOR PROBABILITY FUNCTION

Despite the apparently impressive statistics of the warning system with d’ = 6, if the base rate or a priori probability p is low, then the posterior probability p(threat/alarm) can also be very low:

If p = .001, then the posterior probability p(threat/alarm) = .49

Hence only 1 in 2 alarms will be true alarms.

If p = .0001 the posterior odds of a true alarm are only 1 in 11!

0.5 < p(threat/alarm) = PPV < 1.0

Determine decision criterion ßPPV that leads

to a minimum posterior probability PPV

POSTERIOR PROBABILITY FUNCTION

1.0

0.9

0.8

0.7

Space of admissible

0.6

alarm performance

ß = ß

0.5

PPV

Posterior Probability p(threat/alarm)

0.4

Minimum required

0.3

posterior probability PPV

0.2

Minimum base

0.1

rate b

0.0

0.00

0.02

0.04

0.06

0.08

0.10

A Priori Probability p or Base Rate

For a warning system with d’ = 6, the false alarm rate should not exceed .005, and the posterior probability PPV must exceed .8

Then, if the base rate b is .001, ßPPV must be at least 181.1

The posterior odds of a true alarm with these parameters will then be 8 in 10

However, even better performance could be obtained if d’ could be increased. But by how much?

P(threat/alarm) = P(alarm/threat)

P(alarm/threat) + P(alarm/no threat)([1-base rate]/base rate)

[1]

P(alarm/no threat) = K P(alarm/threat)

[2]

Where K = base rate (1- PPV) / PPV (1 - base rate)

[3]

d = z[p(alarm/threat)) – z(p(alarm/no threat)]

[4]

Equations 1-4 can be displayed as a family of functions for the required minimum d’ for a specified PPV and hit rate

Current radiation detection technologies cannot reach these required levels of d’ (6 and higher)

Likelihood display concept (Sorkin & Kantowitz, 1988)

Number of levels of uncertainty for optimal visualization not clear: N = 3, 4, 5….?

In time-stressed conditions, not much benefit beyond 4 levels (Schinzer et al., 2000)

Likelihood Display Concept

- Levels of resolution
- Example: Danger (D) Uncertain (U) Safe (S)
- D - S
- D - U - C
- D - D/U - U - U/S - S
- Likelihood displays
- improve user diagnostic performance (St. John & Mannes, 2001)
- reduce user workload (Sorkin & Kantowitz, 1988)
- How many levels needed?
- ~ 4 may be sufficient (Schinzer et al., 2000)

Predictive Probabilistic and Temporal Conflict Avoidance Displays

(courtesy of Jason Telner & Paul Milgram, University of Toronto)

Use radiation energy spectra

Use cargo manifest data

Statistical processor

Weighted threshold

Bayesian classifier

Neural network classifier

3 Level Display

No material of concern

Alert – Naturally occurring radioactive material

Alarm – Radioactive threat material

Data Input for Likelihood Display

- Energy spectra for NORMs
- Background
- Fertilizer
- Tile
- Energy spectra for threat material
- Weapons grade Plutonium
- Highly enriched Uranium
- Cargo manifest data
- Available on Dept. of Commerce and Dept. of Transportaion Databases
- Unable to use: separate computer databases, cannot be accessed by Border customs computers

Energy Spectra for NORMs and Threat Material

NORM classification at Middle frequencies

Threat classification at Low frequencies

WGPu = Weapons grade Plutonium

HEU = Highly enriched Uranium

RPM Likelihood Display

Radioactive threat material

Naturally occurring radioactive material

No material of concern

The Bottom Line: RPM Likelihood Display Performance

- Test conducted during 7 month period in 2004 at 1 Crossing
- Compared regular and Likelihood display RPM displays
- Likelihood display RPM with Energy Window Ratio Processor
- N=14 customs personnel
- Paper records of cargo manifest analyzed post hoc
- 1,740 alarms occurred
- 1,617 NORMS
- 123 Medical treatment of driver or passenger

Likelihood Display RPM False Alarm Performance

- 88% reduction of false alarms (from 1,617 to 201) using Energy Window Ratio Likelihood Display
- 100% reduction of false alarms (from 1,617 to 0) if we could have used Energy Window + Cargo Information (post-hoc analysis)
- Medical alarms not affected significantly: these are surrogates for true threat materials

- Current RPMs have high false alarm rates and very low true threat detection rates
- RPM operators have high workload and low trust
- A Bayesian signal detection theory approach can be used to set design criteria for RPM systems with high posterior probability of true threat detection
- A likelihood display concept based on energy spectra and cargo data can enhance RPM performance
- Field study confirms that Likelihood Display RPM associated with lower workload and higher trust

- Fuse computerized cargo manifest database with RPM system
- Use Bayesian or neural network methods to classify threats based on energy spectra, cargo data, and other information
- Expand likelihood uncertainty levels and examine different display candidates
- Validate in field studies

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