Talking About the Assumption in the Room

要旨

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.

著者
Ramaravind Kommiya Mothilal
University of Toronto, Toronto, Ontario, Canada
Faisal M.. Lalani
University of Illinois Urbana-Champaign, Champaign, Illinois, United States
Syed Ishtiaque Ahmed
University of Toronto, Toronto, Ontario, Canada
Shion Guha
University of Toronto, Toronto, Ontario, Canada
Sharifa Sultana
University of Illinois Urbana-Champaign, Champaign, Illinois, United States
DOI

10.1145/3706598.3713958

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713958

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: HCI Methods and Practices

Annex Hall F203
7 件の発表
2025-04-30 01:20:00
2025-04-30 02:50:00
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