Talking About the Assumption in the Room
Not on the map. Why?
Abstract (arXiv)
The reference to assumptions in how practitioners use or interact with machine learning (ML) systems is ubiquitous in HCI and responsible ML discourse. However, what remains unclear from prior works is the conceptualization of assumptions and how practitioners identify and handle assumptions throughout their workflows. This leads to confusion about what assumptions are and what needs to be done with them. We use the concept of an argument from Informal Logic, a branch of Philosophy, to offer a new perspective to understand and explicate the confusions surrounding assumptions. Through semi-structured interviews with 22 ML practitioners, we find what contributes most to these confusions is how independently assumptions are constructed, how reactively and reflectively they are handled, and how nebulously they are recorded. Our study brings the peripheral discussion of assumptions in ML to the center and presents recommendations for practitioners to better think about and work with assumptions.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 42 of its 149 references that are in the map. First posted on arXiv 18 Feb 2025.
Builds on
42 of 149 references.
References · 42
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30 more
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.