# AI Thinking: A framework for rethinking artificial intelligence in practice

arXiv: https://arxiv.org/abs/2409.12922 · HTML: https://mlsanity.com/paper/2409.12922/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 117 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Large Language Models in Finance: A Survey](https://mlsanity.com/paper/2311.10723/) (2023)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey](https://mlsanity.com/paper/2307.04370/) (2023)
- [Problem Formulation and Fairness](https://mlsanity.com/paper/1901.02547/) (2019)
- [Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI](https://mlsanity.com/paper/2105.05424/) (2021)
- [Limits of End-to-End Learning](https://mlsanity.com/paper/1704.08305/) (2017)
- [Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI](https://mlsanity.com/paper/2302.00799/) (2023)
- [Exploring Representativeness and Informativeness for Active Learning](https://mlsanity.com/paper/1904.06685/) (2019)
- [Representativeness in Statistics, Politics, and Machine Learning](https://mlsanity.com/paper/2101.03827/) (2021)
- [A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks](https://mlsanity.com/paper/2110.01434/) (2021)
- [Privacy Intelligence: A Survey on Image Privacy in Online Social Networks](https://mlsanity.com/paper/2008.12199/) (2020)
- [Translational NLP: A New Paradigm and General Principles for Natural Language Processing Research](https://mlsanity.com/paper/2104.07874/) (2021)
- [Definition drives design: Disability models and mechanisms of bias in AI technologies](https://mlsanity.com/paper/2206.08287/) (2022)
- [Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and Health](https://mlsanity.com/paper/2011.13978/) (2020)

## Built on by (most cited first, 2 of 2)

- [Thinking Like a Scientist: Can Interactive Simulations Foster Critical AI Literacy?](https://mlsanity.com/paper/2507.21090/) (2025)
- [Letting the Data Speak: Extracting Keywords from Crowdsourced Collections with AI](https://mlsanity.com/paper/2607.09324/) (2026)

## Abstract (arXiv)

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.