Book contents
- Human-Centered Machine Learning
- Reviews
- Human-Centered Machine Learning
- Copyright page
- Contents
- Contributors
- 1 Introduction
- Part I Human-Centered Machine Learning in the Arts and Humanities
- Part II Doing Human-Centered Machine Learning
- Part III Humans and Machine Learning
- Part IV Machine Learning in Its Social Context
- 12 A Use-Appraise-Modify-Create Learning Progression (UAMC) and a Machine Learning Education Framework (MLEF) for the Development of AI-Engaged Citizens
- 13 Artificially Intelligent Technology for the Margins
- 14 The Anatomy of the Saliency Cropping Leviathan
- Part V Machine Learning and Humanity
- References
13 - Artificially Intelligent Technology for the Margins
Reflecting on Challenges and Opportunities
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
- 1 Introduction
- Part I Human-Centered Machine Learning in the Arts and Humanities
- Part II Doing Human-Centered Machine Learning
- Part III Humans and Machine Learning
- Part IV Machine Learning in Its Social Context
- 12 A Use-Appraise-Modify-Create Learning Progression (UAMC) and a Machine Learning Education Framework (MLEF) for the Development of AI-Engaged Citizens
- 13 Artificially Intelligent Technology for the Margins
- 14 The Anatomy of the Saliency Cropping Leviathan
- Part V Machine Learning and Humanity
- References
Summary
Artificial Intelligence (AI) technologies have often been communicated as holding significant potential to enhance social welfare and empower marginalized communities, addressing critical societal challenges in health, education, and public services. However, whether AI can truly deliver on these promises remains to be determined. This chapter examines both the opportunities and challenges of designing AI technologies for and with marginalized groups, emphasizing the need for inclusive and ethical design practices. While AI can support equitable decision-making, improve access to services, and foster social inclusion, it also risks reinforcing biases, exacerbating inequalities, and creating a vicious cycle of AI-based discrimination that may further marginalize vulnerable populations.
The chapter reviews current research and frameworks to provide actionable insights for creating AI systems that are inclusive, transparent, and contextually relevant. It includes a discussion of methods and frameworks that can be applied to design AI technologies for and with marginalized groups, highlighting the importance of participatory design methods to ensure that the needs, values, and perspectives of these communities are integral to the technology. Through illustrative examples and reflective questions, the chapter offers resources for researchers, students, and designers to address ethical dilemmas and explore the how and why of involving marginalized groups in AI development. This work underscores the imperative to position marginalized communities as co-creators of AI systems to achieve equitable and socially just outcomes.
Information
- Type
- Chapter
- Information
- Human-Centered Machine Learning , pp. 340 - 368Publisher: Cambridge University PressPrint publication year: 2026
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