0
Cognitive Engineering Laboratory
Designing for trust in an automated
decision aid using the abstraction-
decomposition spa...
Cognitive Engineering Laboratory
Human-automation interaction
• Automation is often designed and introduced
without consid...
Cognitive Engineering Laboratory
Basis of trust: communicating
automation capability
• Communicate automation capability t...
Cognitive Engineering Laboratory
Abstraction-decomposition space
(ADS)
Part-whole decomposition
System Subsystems Componen...
Cognitive Engineering Laboratory
ADS as a description of automation
capability
ADS properties → Aspects of automation capa...
Cognitive Engineering Laboratory
Intelligent Drinking Water Monitoring
System (IDWMS)
• Sensor sites distributed
throughou...
Cognitive Engineering Laboratory
ADS of the IDWMS capabilities
7
Cognitive Engineering Laboratory
ADS of the IDWMS capabilities
8
Cognitive Engineering Laboratory
Conclusion
• There is a need to design for appropriate trust in
automated systems in orde...
Cognitive Engineering Laboratory
Questions?
Thank you!
Kevin Kan
kan@mie.utoronto.ca
10
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Kevin kan- the 11th annual Human Factors IUW 2010

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Transcript of "Kevin kan- the 11th annual Human Factors IUW 2010"

  1. 1. Cognitive Engineering Laboratory Designing for trust in an automated decision aid using the abstraction- decomposition space Kevin Kan, M.A.Sc. candidate kan@mie.utoronto.ca Supervisor: Prof. Greg Jamieson November 13, 2010
  2. 2. Cognitive Engineering Laboratory Human-automation interaction • Automation is often designed and introduced without considering how it will be used • Users may reject or overuse the automation • Trust guides automation use; the goal is to have appropriate trust • Appropriateness of trust is relative to automation capability 2
  3. 3. Cognitive Engineering Laboratory Basis of trust: communicating automation capability • Communicate automation capability to influence trust • 2 dimensions to capability information: • How do we gather specific information requirements to inform interface design for a decision aid? 3 Detail General → Specific Attributional abstraction Purpose Process Performance
  4. 4. Cognitive Engineering Laboratory Abstraction-decomposition space (ADS) Part-whole decomposition System Subsystems Components Levelsofabstraction Functional purpose The goals of the system Abstract function The fundamental laws and flows of the system Generalized function The basic processes performed by the system Physical function The physical components of the system Physical form The location and appearance of the components 4 Contains → ← Is a part of Why? ↑ What? ↓ How?
  5. 5. Cognitive Engineering Laboratory ADS as a description of automation capability ADS properties → Aspects of automation capability Levels of abstraction describe the same system using different sets of concepts → Different degrees of attributional abstraction (purpose, process, performance) Higher levels of abstraction are less detailed than lower levels → Different levels of detail Part-whole decomposition → Different levels of detail Means-end links connect levels of abstraction → How purpose, process, and performance are related; how to narrow in on more detail 5
  6. 6. Cognitive Engineering Laboratory Intelligent Drinking Water Monitoring System (IDWMS) • Sensor sites distributed throughout a water distribution network • Data acquired and processed to support water monitoring • A limited set of sensors means that operators must trust the aid appropriately 6
  7. 7. Cognitive Engineering Laboratory ADS of the IDWMS capabilities 7
  8. 8. Cognitive Engineering Laboratory ADS of the IDWMS capabilities 8
  9. 9. Cognitive Engineering Laboratory Conclusion • There is a need to design for appropriate trust in automated systems in order to encourage appropriate use • We can use the ADS to gather specific information requirements that describe the capabilities of a decision aid • … but what is the appropriate format for displaying the identified information requirements? 9
  10. 10. Cognitive Engineering Laboratory Questions? Thank you! Kevin Kan kan@mie.utoronto.ca 10
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