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Trolley crash : approaching key metrics for ethical AI practitioners, researchers, and policy makers /

"The prolific deployment of Artificial Intelligence (AI) across different fields has introduced novel challenges for AI developers and researchers. AI is permeating decision making for the masses, and its applications range from self-driving automobiles to financial loan approvals. With AI maki...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Wu, Peggy (Editor), Salpukas, Michael (Editor), Wu, Xinfu (Editor), Ellsworth, Shannon (Editor)
Format: eBook
Language:English
Published: London, United Kingdom ; San Diego, CA, United States : Academic Press, an imprint of Elsevier, [2024]
Subjects:
Artificial intelligence > Moral and ethical aspects.
Artificial intelligence > Ethics.
Intelligence artificielle > Aspect moral.
Intelligence artificielle > Morale.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Trolley Crash
  • Copyright
  • Contents
  • Contributors
  • Foreword
  • Acknowledgments
  • 1 Introduction
  • 1.1 Ethical AI introduction
  • 1.2 Why ethical AI metrics?
  • 1.3 Ethical AI metric development
  • References
  • 2 Terms and references
  • 2.1 Definition of terms and references
  • 2.2 Discussion
  • 2.3 Conclusion
  • References
  • 3 Boiling the frog: Ethical leniency due to prior exposure to technology
  • 3.1 Introduction
  • 3.2 Background
  • 3.3 Literature review
  • 3.3.1 The use of emotion detection in online contexts
  • 3.3.2 The ethical considerations of emotion detection
  • 3.3.3 Technology acceptance and habituation
  • 3.3.4 Evaluation of technology
  • 3.4 Problem
  • 3.5 Methods
  • 3.5.1 Measures
  • 3.6 Data analysis
  • 3.6.1 Ethical leniency (H1)
  • 3.6.2 Likelihood of adoption (H2)
  • 3.6.3 Known usage
  • 3.6.4 Behavioral effects
  • 3.7 Use cases
  • 3.8 Applications
  • 3.9 Discussion
  • 3.9.1 Ethical evaluation
  • 3.9.2 Adoption
  • 3.9.3 Publicity of usage
  • 3.9.4 Behavior
  • 3.10 Conclusions
  • 3.11 Outlook and future works
  • Notes and acknowledgments
  • References
  • 4 Automated ethical reasoners must be interpretation-capable
  • 4.1 Introduction: Why addressing open-texturedness matters
  • 4.1.1 Contributions
  • 4.2 Interpretive reasoning and the MDIA position
  • 4.3 Benchmark tasks to achieve interpretation-capable AI
  • 4.4 Conclusion
  • Acknowledgments
  • References
  • 5 Towards unifying the descriptive and prescriptive for machine ethics
  • 5.1 Machine learning
  • A gamble with ethics
  • 5.2 Definitions, background, and state of the art
  • 5.3 Is machine learning safe?
  • 5.4 Moral axioms
  • A road to safety
  • 5.4.1 Moral axioms for machine ethics
  • 5.4.2 Grounding norms in moral axioms
  • 5.5 Testing luck as distinguishing between morality and convention
  • 5.5.1 Human judgment of moral vs. conventional transgressions
  • 5.5.2 Formalizing the MCT task
  • 5.5.2.1 Step 1
  • MCT training
  • 5.5.2.2 Step 2
  • MCT testing
  • 5.5.2.3 Step 3
  • Evaluating
  • 5.6 Discussion
  • 5.7 Conclusion
  • Acknowledgments
  • References
  • 6 Competent moral reasoning in robot applications: Inner dialog as a step towards artificial phronesis
  • 6.1 Introduction and motivation
  • 6.2 Background, definitions, and notations
  • 6.2.1 Ethics
  • 6.2.2 Morality
  • 6.2.3 AI ethics
  • 6.2.4 Machine ethics, machine morality, and moral machines
  • 6.2.4.1 Ethical impact agents
  • 6.2.4.2 Artificial ethical agent
  • 6.2.4.3 Artificial moral agent
  • 6.2.5 Machine wisdom
  • 6.2.6 Artificial phronesis
  • 6.2.7 Robot consciousness
  • 6.2.8 Robot's inner speech
  • 6.2.9 Trust in AI
  • 6.2.10 Trust in robotics
  • 6.3 Literature review and state of the art
  • 6.4 Problem/system/application definition
  • 6.4.1 Artificial phronesis and inner speech
  • 6.5 Proposed solution
  • 6.5.1 A proposed experiment to test machine ethical competence