Book contents
- Operationalizing Responsible AI
- Operationalizing Responsible AI
- Copyright page
- Contents
- Contributors
- Preface
- 1 Can AI Be Fair?
- 2 Can AI Be Explainable?
- 3 Can AI Be Psychologically Safe?
- 4 Can AI Be Accountable?
- 5 Can AI Be Consentful?
- 6 Can AI Be Transparent?
- 7 Can AI Be Auditable?
- 8 Can AI Be Contextualized?
- References
8 - Can AI Be Contextualized?
Published online by Cambridge University Press: 24 July 2026
Book contents
- Operationalizing Responsible AI
- Operationalizing Responsible AI
- Copyright page
- Contents
- Contributors
- Preface
- 1 Can AI Be Fair?
- 2 Can AI Be Explainable?
- 3 Can AI Be Psychologically Safe?
- 4 Can AI Be Accountable?
- 5 Can AI Be Consentful?
- 6 Can AI Be Transparent?
- 7 Can AI Be Auditable?
- 8 Can AI Be Contextualized?
- References
Summary
Responsible Artificial Intelligence (RAI) principles are informed by a commitment to attending to local context when designing and deploying Artificial Intelligence (AI) systems. Yet, Artificial Intelligence (AI) systems are developed and deployed across distributed and evolving sociotechnical networks, across many jurisdictions and contexts. Responsible AI practitioners thus face a significant challenge: how to ensure that an AI system is contextually appropriate? That is, how to ensure that AI systems are designed to adapt responsibly to the local social, cultural, legal, and operational settings in which they are used? In this chapter we take up this question and consider practices and opportunities for contextualizing AI during AI development and deployment. In doing so, we draw on Joan Fujimura’s concept of articulation work and illustrate methods for contextualizing AI through a case study of AI evaluation in the cultural heritage sector.
Information
- Type
- Chapter
- Information
- Operationalizing Responsible AI , pp. 163 - 188Publisher: Cambridge University PressPrint publication year: 2026
References
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Single logical reading order
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
Short alternative textual descriptions
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
Full alternative textual descriptions
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
Visualised data also available as non-graphical data
You can access graphs or charts in a text or tabular format, so you are not excluded if you cannot process visual displays.
You can access graphs or charts in a text or tabular format, so you are not excluded if you cannot process visual displays.
Visual Accessibility
Use of high contrast between text and background colour
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.