AI Thinking: A framework for rethinking artificial intelligence in practice
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ID: 283221
2024
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Abstract
Artificial intelligence is transforming the way we work with information
across disciplines and practical contexts. A growing range of disciplines are
now involved in studying, developing, and assessing the use of AI in practice,
but these disciplines often employ conflicting understandings of what AI is and
what is involved in its use. New, interdisciplinary approaches are needed to
bridge competing conceptualisations of AI in practice and help shape the future
of AI use. I propose a novel conceptual framework called AI Thinking, which
models key decisions and considerations involved in AI use across disciplinary
perspectives. The AI Thinking model addresses five practice-based competencies
involved in applying AI in context: motivating AI use in information processes,
formulating AI methods, assessing available tools and technologies, selecting
appropriate data, and situating AI in the sociotechnical contexts it is used
in. A hypothetical case study is provided to illustrate the application of AI
Thinking in practice. This article situates AI Thinking in broader
cross-disciplinary discourses of AI, including its connections to ongoing
discussions around AI literacy and AI-driven innovation. AI Thinking can help
to bridge divides between academic disciplines and diverse contexts of AI use,
and to reshape the future of AI in practice.
| Reference Key |
newman-griffis2024ai
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|---|---|
| Authors | Denis Newman-Griffis |
| Journal | arXiv |
| Year | 2024 |
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