# Unpacking Human-AI interactions: From interaction primitives to a design space

arXiv: https://arxiv.org/abs/2401.05115 · HTML: https://mlsanity.com/paper/2401.05115/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 121 references that are in the map.
- First posted on arXiv 10 Jan 2024.

## Builds on (most cited first, 30 of 31)

- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle](https://mlsanity.com/paper/1901.10002/) (2019)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty](https://mlsanity.com/paper/2011.07586/) (2020)
- [Human-Centered Explainable AI (XAI): From Algorithms to User Experiences](https://mlsanity.com/paper/2110.10790/) (2021)
- [A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts](https://mlsanity.com/paper/2105.07190/) (2021)
- [Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach](https://mlsanity.com/paper/2002.01092/) (2020)
- [Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems](https://mlsanity.com/paper/2204.07666/) (2022)
- [Leveraging Explanations in Interactive Machine Learning: An Overview](https://mlsanity.com/paper/2207.14526/) (2022)
- [Conceptual Metaphors Impact Perceptions of Human-AI Collaboration](https://mlsanity.com/paper/2008.02311/) (2020)
- [A systematic review and taxonomy of explanations in decision support and recommender systems](https://mlsanity.com/paper/2006.08672/) (2020)
- [Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases](https://mlsanity.com/paper/2102.11965/) (2021)
- [The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systems](https://mlsanity.com/paper/2105.03354/) (2021)
- [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)
- [Towards A Process Model for Co-Creating AI Experiences](https://mlsanity.com/paper/2104.07595/) (2021)
- [Meaningful human control: actionable properties for AI system development](https://mlsanity.com/paper/2112.01298/) (2021)
- [Mapping the Design Space of Human-AI Interaction in Text Summarization](https://mlsanity.com/paper/2206.14863/) (2022)
- [Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Design](https://mlsanity.com/paper/2303.02884/) (2023)
- [FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions](https://mlsanity.com/paper/1911.12587/) (2019)
- [Towards a multi-stakeholder value-based assessment framework for algorithmic systems](https://mlsanity.com/paper/2205.04525/) (2022)
- [A domain-specific language for describing machine learning datasets](https://mlsanity.com/paper/2207.02848/) (2022)
- [Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations](https://mlsanity.com/paper/2009.06433/) (2020)
- [Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning](https://mlsanity.com/paper/2009.09575/) (2020)
- [Designing AI Learning Experiences for K-12: Emerging Works, Future Opportunities and a Design Framework](https://mlsanity.com/paper/2009.10228/) (2020)
- [Perspectives on Incorporating Expert Feedback into Model Updates](https://mlsanity.com/paper/2205.06905/) (2022)
- [Mapping the Design Space of Interactions in Human-AI Text Co-creation Tasks](https://mlsanity.com/paper/2303.06430/) (2023)
- [On Interactive Machine Learning and the Potential of Cognitive Feedback](https://mlsanity.com/paper/2003.10365/) (2020)

## Built on by (most cited first, 7 of 7)

- [Multi-Turn Human-LLM Interaction Through the Lens of a Two-Way Intelligibility Protocol](https://mlsanity.com/paper/2410.20600/) (2024)
- [The Unbearable Lightness of Prompting: A Critical Reflection on the Environmental Impact of genAI use in Design Education](https://mlsanity.com/paper/2501.16061/) (2025)
- [Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review](https://mlsanity.com/paper/2509.12034/) (2025)
- [X-SYS: A Reference Architecture for Interactive Explanation Systems](https://mlsanity.com/paper/2602.12748/) (2026)
- [The Initial Exploration Problem in Knowledge Graph Exploration](https://mlsanity.com/paper/2602.21066/) (2026)
- [Exploiting Expertise of Non-Expert and Diverse Agents in Social Bandit Learning: A Free Energy Approach](https://mlsanity.com/paper/2603.11757/) (2026)
- [From Overload to Convergence: Supporting Multi-Issue Human-AI Negotiation with Bayesian Visualization](https://mlsanity.com/paper/2603.22766/) (2026)

## Abstract (arXiv)

This paper aims to develop a semi-formal design space for Human-AI interactions, by building a set of interaction primitives which specify the communication between users and AI systems during their interaction. We show how these primitives can be combined into a set of interaction patterns which can provide an abstract specification for exchanging messages between humans and AI/ML models to carry out purposeful interactions. The motivation behind this is twofold: firstly, to provide a compact generalisation of existing practices, that highlights the similarities and differences between systems in terms of their interaction behaviours; and secondly, to support the creation of new systems, in particular by opening the space of possibilities for interactions with models. We present a short literature review on frameworks, guidelines and taxonomies related to the design and implementation of HAI interactions, including human-in-the-loop, explainable AI, as well as hybrid intelligence and collaborative learning approaches. From the literature review, we define a vocabulary for describing information exchanges in terms of providing and requesting particular model-specific data types. Based on this vocabulary, a message passing model for interactions between humans and models is presented, which we demonstrate can account for existing systems and approaches. Finally, we build this into design patterns as mid-level constructs that capture common interactional structures. We discuss how this approach can be used towards a design space for Human-AI interactions that creates new possibilities for designs as well as keeping track of implementation issues and concerns.