# Grasping AI: experiential exercises for designers

arXiv: https://arxiv.org/abs/2310.01282 · HTML: https://mlsanity.com/paper/2310.01282/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 153 references that are in the map.
- First posted on arXiv 2 Oct 2023.

## Builds on (most cited first, 20 of 20)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Revealing the Dark Secrets of BERT](https://mlsanity.com/paper/1908.08593/) (2019)
- [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://mlsanity.com/paper/2109.06977/) (2021)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach](https://mlsanity.com/paper/2002.01092/) (2020)
- [Conceptual Metaphors Impact Perceptions of Human-AI Collaboration](https://mlsanity.com/paper/2008.02311/) (2020)
- [Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation](https://mlsanity.com/paper/2010.11411/) (2020)
- [Towards A Process Model for Co-Creating AI Experiences](https://mlsanity.com/paper/2104.07595/) (2021)
- [Ethics Sheets for AI Tasks](https://mlsanity.com/paper/2107.01183/) (2021)
- [Meaningful human control: actionable properties for AI system development](https://mlsanity.com/paper/2112.01298/) (2021)
- [Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry](https://mlsanity.com/paper/2101.04035/) (2021)
- [Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report](https://mlsanity.com/paper/2210.15767/) (2022)
- [Towards a multi-stakeholder value-based assessment framework for algorithmic systems](https://mlsanity.com/paper/2205.04525/) (2022)
- [Reflexive Design for Fairness and Other Human Values in Formal Models](https://mlsanity.com/paper/2010.05084/) (2020)
- [Explainable Computational Creativity](https://mlsanity.com/paper/2205.05682/) (2022)
- [The Entoptic Field Camera as Metaphor-Driven Research-through-Design with AI Technologies](https://mlsanity.com/paper/2301.09545/) (2023)
- [Respect as a Lens for the Design of AI Systems](https://mlsanity.com/paper/2206.07555/) (2022)
- [Blockchain and Beyond: Understanding Blockchains through Prototypes and Public Engagement](https://mlsanity.com/paper/2112.11891/) (2021)
- [Excavating "Excavating AI": The Elephant in the Gallery](https://mlsanity.com/paper/2009.01215/) (2020)

## Built on by (most cited first, 3 of 3)

- [(Un)making AI Magic: a Design Taxonomy](https://mlsanity.com/paper/2403.15216/) (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)
- [Comparing Socio-technical Design Principles with Guidelines for Human-centered AI](https://mlsanity.com/paper/2607.10331/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into the functioning of physical and digital products, creating unprecedented opportunities for interaction and functionality. However, there is a challenge for designers to ideate within this creative landscape, balancing the possibilities of technology with human interactional concerns. We investigate techniques for exploring and reflecting on the interactional affordances, the unique relational possibilities, and the wider social implications of AI systems. We introduced into an interaction design course (n=100) nine 'AI exercises' that draw on more than human design, responsible AI, and speculative enactment to create experiential engagements around AI interaction design. We find that exercises around metaphors and enactments make questions of training and learning, privacy and consent, autonomy and agency more tangible, and thereby help students be more reflective and responsible on how to design with AI and its complex properties in both their design process and outcomes.