Predicting and explaining with machine learning models: Social science as a touchstone
Abstract
Machine learning (ML) models recently led to major breakthroughs in predictive tasks in the natural sciences. Yet their benefits for the social sciences are less evident, as even high-profile studies on the prediction of life trajectories have shown to be largely unsuccessful - at least when measured in traditional criteria of scientific success. This paper tries to shed light on this remarkable performance gap. Comparing two social science case studies to a paradigm example from the natural sciences, we argue that, in addition to explanation, prediction is an important goal of social science - and we identify constraints that impede pure ML prediction from being successful in that field. As a remedy, we outline elements of an integrative modelling approach that combines explanatory models and predictive ML models.
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ML models have limited prediction value when applied to complex social phenomena, in contrast to their success in physical and biological contexts.
“This limited prediction value highlights the potential limitations of ML models when applied to complex social phenomena, contrasting with their success in physical and biological contexts [66, 67].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [67]
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- Authors
- Buchholz, O., & Grote, T.
- Affiliations
- University of Tübingen (DE)
- Corresponding author
- Buchholz, Oliver
- Venue
- Studies in History and Philosophy of Science
- Publisher
- Elsevier BV
- Published
- 2023-12
- Volume
- 102
- Pages
- 60-69
- ISSN
- 0039-3681 (ISSN-L)
- OpenAlex
- W4388064706
- Cited by
- 15
- References
- 72
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 4
- FWCI
- 13.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 1065 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Studies in History and Philosophy of Science Part A (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Fund for Folk Culture
- Baden-Württemberg Stiftung
- Deutsche Forschungsgemeinschaft
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- machine learning, prediction, explanation, predictive success, integrative modeling, explanatory models, philosophy of social science, natural sciences versus social sciences
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