Trolley crash : approaching key metrics for ethical AI practitioners, researchers, and policy makers / edited by Peggy Wu, Michael Salpukas, Hsin-Fu Wu, Shannon Ellsworth.

Format:

Book

Contributor:

Wu, Peggy, editor.

Salpukas, Michael, editor.

Wu, Xinfu, editor.

Ellsworth, Shannon, editor.

ScienceDirect (Online service)

Language:

English

Subjects (All):

Artificial intelligence--Moral and ethical aspects.

Artificial intelligence.

Artificial intelligence--Ethics.

Physical Description:

1 online resource (xv, 248 pages) : color illustrations

Place of Publication:

London, United Kingdom ; San Diego, CA, United States : Academic Press, an imprint of Elsevier, [2024]

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

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

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

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

Notes:

Includes blibliographical references and index.

Electronic reproduction. Amsterdam Available via World Wide Web.

Description based on online resource; title from digital title page (viewed on February 29, 2024).

Other Format:

Print version:

ISBN:

9780443159923

0443159920

Publisher Number:

40032201203

Access Restriction:

Restricted for use by site license.