Book contents
- Human-Centered Machine Learning
- Reviews
- Human-Centered Machine Learning
- Copyright page
- Contents
- Contributors
- 1Introduction
- Part IHuman-Centered Machine Learning in the Arts and Humanities
- Part IIDoing Human-Centered Machine Learning
- Part IIIHumans and Machine Learning
- Part IVMachine Learning in Its Social Context
- 12A Use-Appraise-Modify-Create Learning Progression (UAMC) and a Machine Learning Education Framework (MLEF) for the Development of AI-Engaged Citizens
- 13Artificially Intelligent Technology for the Margins
- 14The Anatomy of the Saliency Cropping Leviathan
- Part VMachine Learning and Humanity
12 - A Use-Appraise-Modify-Create Learning Progression (UAMC) and a Machine Learning Education Framework (MLEF) for the Development of AI-Engaged Citizens
from Part IV - Machine Learning in Its Social Context
Published online by Cambridge University Press: 11 September 2026
Book contents
- Human-Centered Machine Learning
- Reviews
- Human-Centered Machine Learning
- Copyright page
- Contents
- Contributors
- 1Introduction
- Part IHuman-Centered Machine Learning in the Arts and Humanities
- Part IIDoing Human-Centered Machine Learning
- Part IIIHumans and Machine Learning
- Part IVMachine Learning in Its Social Context
- 12A Use-Appraise-Modify-Create Learning Progression (UAMC) and a Machine Learning Education Framework (MLEF) for the Development of AI-Engaged Citizens
- 13Artificially Intelligent Technology for the Margins
- 14The Anatomy of the Saliency Cropping Leviathan
- Part VMachine Learning and Humanity
Summary
AI and machine learning are everywhere, influencing everything from social media feeds to hiring and criminal justice. Yet, most people don’t understand how these systems work or their potential risks. To ensure AI serves society fairly, we need more than just technical experts—we need informed citizens who can critically evaluate and shape AI policies. This chapter introduces the Use-Appraise-Modify-Create (UAMC) learning progression, adding an essential role: AI Appraisers—people who can recognize, assess, and discuss AI’s impact. We also propose the Machine Learning Education Framework (MLEF), which emphasizes not just technical skills but also AI bias, ethics, and societal implications. By analyzing AI education across different levels, we find gaps between technical training and ethical understanding. UAMC and MLEF aim to bridge this, helping more people move from passive AI users to active contributors in shaping AI’s future.
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- Type
- Chapter
- Information
- Human-Centered Machine Learning , pp. 313 - 339Publisher: Cambridge University PressPrint publication year: 2026
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