A Comparative Analysis of Industry Human-AI Interaction Guidelines
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Abstract (arXiv)
With the recent release of AI interaction guidelines from Apple, Google, and Microsoft, there is clearly interest in understanding the best practices in human-AI interaction. However, industry standards are not determined by a single company, but rather by the synthesis of knowledge from the whole community. We have surveyed all of the design guidelines from each of these major companies and developed a single, unified structure of guidelines, giving developers a centralized reference. We have then used this framework to compare each of the surveyed companies to find differences in areas of emphasis. Finally, we encourage people to contribute additional guidelines from other companies, academia, or individuals, to provide an open and extensible reference of AI design guidelines at https://ai-open-guidelines.readthedocs.io/.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 2; 2025: 2; 2026: 1.
10 papers.
- 2024Design Principles for Generative AI Applicationscited by 57
- 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototypingcited by 28
- 2023Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiencescited by 21
- 2022GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward Informationcited by 13
- 2021NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networkscited by 12
- 2025SPHERE: An Evaluation Card for Human-AI Systemscited by 9
- 2026Reflections and New Directions for Human-Centered Large Language Modelscited by 2
- 2025Human-AI Interaction and User Satisfaction: Empirical Evidence from Online Reviews of AI Productscited by 1
- 2022A Design Space for Explainable Ranking and Ranking Modelscited by 0
- 2023UX Heuristics and Checklist for Deep Learning powered Mobile Applications with Image Classificationcited by 0
- 2026Reflections and New Directions for Human-Centered Large Language Models7 May 2026
- 2025SPHERE: An Evaluation Card for Human-AI Systems24 Mar 2025
- 2025Human-AI Interaction and User Satisfaction: Empirical Evidence from Online Reviews of AI Products23 Mar 2025
- 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototyping23 Feb 2024
- 2024Design Principles for Generative AI Applications25 Jan 2024
- 2023Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences6 Oct 2023
- 2023UX Heuristics and Checklist for Deep Learning powered Mobile Applications with Image Classification5 Jul 2023
- 2022A Design Space for Explainable Ranking and Ranking Models27 May 2022
- 2022GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward Information2 Feb 2022
- 2021NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks29 Aug 2021