# Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry

arXiv: https://arxiv.org/abs/2101.04035 · HTML: https://mlsanity.com/paper/2101.04035/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 86 references that are in the map.
- First posted on arXiv 11 Jan 2021.

## Builds on (most cited first, 5 of 5)

- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Experiential AI](https://mlsanity.com/paper/1908.02619/) (2019)

## Built on by (most cited first, 14 of 14)

- [Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience](https://mlsanity.com/paper/2302.10395/) (2023)
- [AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development](https://mlsanity.com/paper/2502.18682/) (2025)
- [Contestable Camera Cars: A Speculative Design Exploration of Public AI That Is Open and Responsive to Dispute](https://mlsanity.com/paper/2302.04603/) (2023)
- [The Entoptic Field Camera as Metaphor-Driven Research-through-Design with AI Technologies](https://mlsanity.com/paper/2301.09545/) (2023)
- [(Un)making AI Magic: a Design Taxonomy](https://mlsanity.com/paper/2403.15216/) (2024)
- [Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretability](https://mlsanity.com/paper/2109.11849/) (2021)
- [Algorithmic Ways of Seeing: Using Object Detection to Facilitate Art Exploration](https://mlsanity.com/paper/2403.19174/) (2024)
- [Grasping AI: experiential exercises for designers](https://mlsanity.com/paper/2310.01282/) (2023)
- [Responding to Generative AI Technologies with Research-through-Design: The Ryelands AI Lab as an Exploratory Study](https://mlsanity.com/paper/2405.04677/) (2024)
- [Reimagining Personal Data: Unlocking the Potential of AI-Generated Images in Personal Data Meaning-Making](https://mlsanity.com/paper/2502.18853/) (2025)
- [Lessons from the Development of an Anomaly Detection Interface on the Mars Perseverance Rover using the ISHMAP Framework](https://mlsanity.com/paper/2302.07187/) (2023)
- [TimeCapsule: Generative Hallucination as a Method for Historical Sensemaking](https://mlsanity.com/paper/2607.24750/) (2026)
- [Human-AI Coordination Zones: A Framework for Designing Human-in-the-Loop Experiences with Agentic AI](https://mlsanity.com/paper/2606.09848/) (2026)
- [Architecture and Affordances of PLAUD: Performative Latents and Unsupervised DDSP](https://mlsanity.com/paper/2608.13724/) (2026)

## Abstract (arXiv)

Design research is important for understanding and interrogating how emerging technologies shape human experience. However, design research with Machine Learning (ML) is relatively underdeveloped. Crucially, designers have not found a grasp on ML uncertainty as a design opportunity rather than an obstacle. The technical literature points to data and model uncertainties as two main properties of ML. Through post-phenomenology, we position uncertainty as one defining material attribute of ML processes which mediate human experience. To understand ML uncertainty as a design material, we investigate four design research case studies involving ML. We derive three provocative concepts: thingly uncertainty: ML-driven artefacts have uncertain, variable relations to their environments; pattern leakage: ML uncertainty can lead to patterns shaping the world they are meant to represent; and futures creep: ML technologies texture human relations to time with uncertainty. Finally, we outline design research trajectories and sketch a post-phenomenological approach to human-ML relations.