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 making decisions that have ethical implications, responsibilities are now being pushed to AI designers who may be far-removed from how, where, and when these ethical decisions occur. Trolley Crash: Approaching Key Metrics for Ethical AI Practitioners, Researchers, and Policy Makers provides audiences with a catalogue of perspectives and methodologies from the latest research in ethical computing. This work integrates philosophical and computational approaches into a unified framework for ethical reasoning in the current AI landscape, specifically focusing on approaches for developing metrics. Written for AI researchers, ethicists, computer scientists, software engineers, operations researchers, and autonomous systems designers and developers, Trolley Crash will be a welcome reference for those who wish to better understand metrics for ethical reasoning in autonomous systems and related computational applications.
1. Chapter One Introduction - p. 1 2. Chapter Two Terms and references - p. 7 3. Chapter Three Boiling the frog: Ethical leniency due to prior exposure to technology - p. 33 4. Chapter Four Automated ethical reasoners must be interpretation-capable - p. 52 5. Chapter Five Towards unifying the descriptive and prescriptive for machine ethics - p. 69 6. Chapter Six Competent moral reasoning in robot applications: Inner dialog as a step towards artificial phronesis - p. 89 7. Chapter Seven Autonomy compliance with doctrine and ethics by using ontological frameworks - p. 106 8. Chapter Eight Meaningful human control and ethical neglect tolerance: Initial thoughts on how to define, model, and measure them - p. 125 9. Chapter Nine Continuous automation approach for autonomous ethics-based audit of AI systems - p. 141 10. Chapter Ten A tiered approach for ethical AI evaluation metrics - p. 163 11. Chapter Eleven Designing meaningful metrics to demonstrate ethical supervision of autonomous systems - p. 189 12. Chapter Twelve Obtaining hints to understand language model-based moral decision making by generating consequences of acts - p. 209 13. Chapter Thirteen Emerging issues and challenges - p. 224 14. Acronyms - p. 238 15. Index - p. 242
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 making decisions that have ethical implications, responsibilities are now being pushed to AI designers who may be far-removed from how, where, and when these ethical decisions occur. Trolley Crash: Approaching Key Metrics for Ethical AI Practitioners, Researchers, and Policy Makers provides audiences with a catalogue of perspectives and methodologies from the latest research in ethical computing. This work integrates philosophical and computational approaches into a unified framework for ethical reasoning in the current AI landscape, specifically focusing on approaches for developing metrics. Written for AI researchers, ethicists, computer scientists, software engineers, operations researchers, and autonomous systems designers and developers, Trolley Crash will be a welcome reference for those who wish to better understand metrics for ethical reasoning in autonomous systems and related computational applications.
1. Chapter One Introduction - p. 1 2. Chapter Two Terms and references - p. 7 3. Chapter Three Boiling the frog: Ethical leniency due to prior exposure to technology - p. 33 4. Chapter Four Automated ethical reasoners must be interpretation-capable - p. 52 5. Chapter Five Towards unifying the descriptive and prescriptive for machine ethics - p. 69 6. Chapter Six Competent moral reasoning in robot applications: Inner dialog as a step towards artificial phronesis - p. 89 7. Chapter Seven Autonomy compliance with doctrine and ethics by using ontological frameworks - p. 106 8. Chapter Eight Meaningful human control and ethical neglect tolerance: Initial thoughts on how to define, model, and measure them - p. 125 9. Chapter Nine Continuous automation approach for autonomous ethics-based audit of AI systems - p. 141 10. Chapter Ten A tiered approach for ethical AI evaluation metrics - p. 163 11. Chapter Eleven Designing meaningful metrics to demonstrate ethical supervision of autonomous systems - p. 189 12. Chapter Twelve Obtaining hints to understand language model-based moral decision making by generating consequences of acts - p. 209 13. Chapter Thirteen Emerging issues and challenges - p. 224 14. Acronyms - p. 238 15. Index - p. 242
