Linked e-resources
Details
Table of Contents
Intro
Preface
Contents
Editors and Contributors
Part I Artificial Intelligence and Agents
1 Toward Behavioral AI: Cognitive Factors Underlying the Public Psychology of Artificial Intelligence
A Revived Human View of Artificial Intelligence
India
New Zealand
United Kingdom
United States
China
Worldwide Growth and An Evolving Need for Behavioral AI
Why Should We Trust Algorithms?
Algorithm Aversion and Appreciation
Domain Specificity and Task Sensitivity
Measuring Preferences for AI Versus Humans in Light of National AI Strategies
Cognitive Factors Related to Preferences Toward AI Algorithms
Transparency and Explanation
Algorithmic Error
Perceived Understanding
Accuracy and Risk Levels
Sense of Uniqueness-Neglect and Responsibility
Summary of Factors
Cognitive Solutions to Increase Acceptability of AI and Enhance Algorithmic Appreciation
Communicate Transparency of Algorithmic Processing
Give Control to Modify Algorithmic Outcomes
Provide Social Proof
Increase Understanding
Frame Algorithms to Be More Humanlike
Conclusion
References
2 Defining the Relationship Between the Level of Autonomy in a Computer and the Cognitive Workload of Its User
Introduction
Review
Levels of Automation
Measurements of Mental Workload
Other Factors that May Impact Relationship
Experimental Design and Methodology
Discussion
Conclusion
References
3 Cognitive Effects of the Anthropomorphization of Artificial Agents in Human-Agent Interactions
Introduction
Anthropomorphic Design
The Uncanny Valley
Social Robotics
Empathy and Attribution of Mind
Physical Human-Robot Interaction
Goal-directed Action and Mirroring
Altruistic and Strategic Behavior Toward Robots
Ethical Considerations
Present Challenges
State of the Field
References
Part II Decision Support and Assistance Systems
4 Psychological Factors Impacting Adoption of Decision Support Tools
Introduction
Decision Support Systems
Adoption
Cognitive Biases
Anchoring Bias
Egocentric Bias
Belief Bias
Familiarity Bias
Automation Bias
Trust
Ethics and Culpability
Mitigating Factors
Requirements
Design
Training
Release
Support
Conclusion
Preface
Contents
Editors and Contributors
Part I Artificial Intelligence and Agents
1 Toward Behavioral AI: Cognitive Factors Underlying the Public Psychology of Artificial Intelligence
A Revived Human View of Artificial Intelligence
India
New Zealand
United Kingdom
United States
China
Worldwide Growth and An Evolving Need for Behavioral AI
Why Should We Trust Algorithms?
Algorithm Aversion and Appreciation
Domain Specificity and Task Sensitivity
Measuring Preferences for AI Versus Humans in Light of National AI Strategies
Cognitive Factors Related to Preferences Toward AI Algorithms
Transparency and Explanation
Algorithmic Error
Perceived Understanding
Accuracy and Risk Levels
Sense of Uniqueness-Neglect and Responsibility
Summary of Factors
Cognitive Solutions to Increase Acceptability of AI and Enhance Algorithmic Appreciation
Communicate Transparency of Algorithmic Processing
Give Control to Modify Algorithmic Outcomes
Provide Social Proof
Increase Understanding
Frame Algorithms to Be More Humanlike
Conclusion
References
2 Defining the Relationship Between the Level of Autonomy in a Computer and the Cognitive Workload of Its User
Introduction
Review
Levels of Automation
Measurements of Mental Workload
Other Factors that May Impact Relationship
Experimental Design and Methodology
Discussion
Conclusion
References
3 Cognitive Effects of the Anthropomorphization of Artificial Agents in Human-Agent Interactions
Introduction
Anthropomorphic Design
The Uncanny Valley
Social Robotics
Empathy and Attribution of Mind
Physical Human-Robot Interaction
Goal-directed Action and Mirroring
Altruistic and Strategic Behavior Toward Robots
Ethical Considerations
Present Challenges
State of the Field
References
Part II Decision Support and Assistance Systems
4 Psychological Factors Impacting Adoption of Decision Support Tools
Introduction
Decision Support Systems
Adoption
Cognitive Biases
Anchoring Bias
Egocentric Bias
Belief Bias
Familiarity Bias
Automation Bias
Trust
Ethics and Culpability
Mitigating Factors
Requirements
Design
Training
Release
Support
Conclusion