Book contents
- Frontmatter
- Dedication
- Contents
- Abbreviations
- Preface
- Acknowledgments
- Part I Preliminary Considerations
- Part II Evaluation for Classification
- Part III Evaluation for Other Settings
- Part IV Evaluation from a Practical Perspective
- 10 Industrial-Strength Evaluation
- 11 Responsible Machine Learning
- 12 Conclusion
- Appendices
- References
- Index
12 - Conclusion
from Part IV - Evaluation from a Practical Perspective
Published online by Cambridge University Press: 07 November 2024
- Frontmatter
- Dedication
- Contents
- Abbreviations
- Preface
- Acknowledgments
- Part I Preliminary Considerations
- Part II Evaluation for Classification
- Part III Evaluation for Other Settings
- Part IV Evaluation from a Practical Perspective
- 10 Industrial-Strength Evaluation
- 11 Responsible Machine Learning
- 12 Conclusion
- Appendices
- References
- Index
Summary
Chapter 12 is the conclusion. It presents a discussion of how the components of performance evaluation for learning algorithms discussed throughout the book unify into an overall framework for in-laboratory evaluation. This is followed by a discussion of how to move from a laboratory setting to a deployment setting based on the material covered in the last part of the book. We then discuss the potential social consequences of machine learning technology deployment together with their causes, and advocate for the consideration of these consequences as part of the evaluation framework. We follow this discussion with a few concluding remarks.
- Type
- Chapter
- Information
- Machine Learning EvaluationTowards Reliable and Responsible AI, pp. 342 - 358Publisher: Cambridge University PressPrint publication year: 2024