# Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretability

arXiv: https://arxiv.org/abs/2109.11849 · HTML: https://mlsanity.com/paper/2109.11849/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 83 references that are in the map.
- First posted on arXiv 24 Sep 2021.

## Builds on (most cited first, 16 of 16)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021)
- [Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making](https://mlsanity.com/paper/1902.02960/) (2019)
- ['It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions](https://mlsanity.com/paper/1801.10408/) (2018)
- [Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach](https://mlsanity.com/paper/2002.01092/) (2020)
- [Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences](https://mlsanity.com/paper/1712.00547/) (2017)
- [Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs](https://mlsanity.com/paper/2004.11440/) (2020)
- [CheXplain: Enabling Physicians to Explore and UnderstandData-Driven, AI-Enabled Medical Imaging Analysis](https://mlsanity.com/paper/2001.05149/) (2020)
- ["The Human Body is a Black Box": Supporting Clinical Decision-Making with Deep Learning](https://mlsanity.com/paper/1911.08089/) (2019)
- [Question-Driven Design Process for Explainable AI User Experiences](https://mlsanity.com/paper/2104.03483/) (2021)
- [Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry](https://mlsanity.com/paper/2101.04035/) (2021)
- [Dice in the Black Box: User Experiences with an Inscrutable Algorithm](https://mlsanity.com/paper/1812.03219/) (2018)
- [Interpreting Deep Neural Networks Through Variable Importance](https://mlsanity.com/paper/1901.09839/) (2019)
- [Space of Reasons and Mathematical Model](https://mlsanity.com/paper/2007.02489/) (2020)

## Built on by (most cited first, 4 of 4)

- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [The Entoptic Field Camera as Metaphor-Driven Research-through-Design with AI Technologies](https://mlsanity.com/paper/2301.09545/) (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)
- [Not All Subjectivity Is the Same! Defining Desiderata for the Evaluation of Subjectivity in NLP](https://mlsanity.com/paper/2603.28351/) (2026)

## Abstract (arXiv)

During a research project in which we developed a machine learning (ML) driven visualization system for non-ML experts, we reflected on interpretability research in ML, computer-supported collaborative work and human-computer interaction. We found that while there are manifold technical approaches, these often focus on ML experts and are evaluated in decontextualized empirical studies. We hypothesized that participatory design research may support the understanding of stakeholders' situated sense-making in our project, yet, found guidance regarding ML interpretability inexhaustive. Building on philosophy of technology, we formulated explanation strategies as an empirical-analytical lens explicating how technical explanations mediate the contextual preferences concerning people's interpretations. In this paper, we contribute a report of our proof-of-concept use of explanation strategies to analyze a co-design workshop with non-ML experts, methodological implications for participatory design research, design implications for explanations for non-ML experts and suggest further investigation of technological mediation theories in the ML interpretability space.