Situating machine learning: On the calibration of problems in practice
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
In this paper, we employ John Dewey’s notion of the situation as an analytic lens for observing and theorizing machine learning. Based on two ethnographic case studies in art and science, we account for machine learning as practice and examine the dynamics of the situations it gives rise to. Following Dewey, our observations focus on the transformation of situations from an initial state of indeterminacy through to problematizations and their resolution. Rethinking machine learning through the situation, we analyze how cooperating machine learners, both human and non-human, resolve situations and thereby refine their mutual attunement. With Dewey, we first explain how machine learners train through disruption and adaptation as they identify and solve problems. Second, we show that these problems concern issues of latency and addressability in efforts of cooperation between heterogeneous machine learners. Third, we discuss how machine learning practices cultivate situations that feature careful calibrations of problems that allow for their productive transformation. Our empirically grounded approach offers a pragmatist account of machine learning as a continually indeterminate and dynamic situated practice. As a contribution to ongoing discussions in social theory, we reframe existing characterizations of machine learning as issues of latency and addressability in cooperation.
Details
Original language | English |
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Pages (from-to) | 315-337 |
Number of pages | 23 |
Journal | Distinktion : scandinavian journal of social theory |
Volume | 24 |
Issue number | 2 |
Early online date | Feb 2023 |
Publication status | Published - 25 Feb 2023 |
Peer-reviewed | Yes |
External IDs
WOS | 000941694400001 |
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Scopus | 85149373864 |
Mendeley | 0b661400-a910-3eed-8d7b-0fae4adc52da |
ORCID | /0000-0003-4433-8428/work/148144833 |
ORCID | /0000-0003-0533-9698/work/171065298 |
Keywords
Research priority areas of TU Dresden
DFG Classification of Subject Areas according to Review Boards
- Public Law
- Historical Linguistics
- Applied Linguistics, Experimental Linguistics, Computational Linguistics
- Engineering Design, Machine Elements, Product Development
- Business Administration
- European and American Literature
- Theatre and Media Studies
- Sociological Theory
- Urbanism, Spatial Planning, Transportation and Infrastructure Planning, Landscape Planning
- Theoretical Philosophy
- Security and Dependability
- General, Cognitive and Mathematical Psychology
- Human Geography
- Empirical Social Research
- Communication Sciences
Subject groups, research areas, subject areas according to Destatis
- Communication and Information Technology
- Business Administration
- Media Science/Studies
- American Language and Literature / American Studies
- Urban Planning and Settlement
- Computer science (general)
- Sociology
- Civil Law
- Didactics of Geography
- Philosophy (general)
- Mechanical Engineering
- General and Cognitive Psychology
- Applied Linguistics, Vocational training in foreign languages
Sustainable Development Goals
- SDG 17 - Partnerships for the Goals
- SDG 14 - Life Below Water
- SDG 8 - Decent Work and Economic Growth
- SDG 9 - Industry, Innovation, and Infrastructure
- SDG 16 - Peace, Justice and Strong Institutions
- SDG 10 - Reduced Inequalities
- SDG 11 - Sustainable Cities and Communities
- SDG 13 - Climate Action
- SDG 4 - Quality Education
- SDG 12 - Responsible Consumption and Production
Keywords
- Artificial intelligence, Contingency, Ethnography, Indeterminacy, Machine learning, Pragmatism, Situation, Technology, technology, machine learning, pragmatism, contingency, indeterminacy, ethnography, situation