Inductive Risk and Values in Science
Abstract
Although epistemic values have become widely accepted as part of scientific reasoning, non-epistemic values have been largely relegated to the “external” parts of science (the selection of hypotheses, restrictions on methodologies, and the use of scientific technologies). I argue that because of inductive risk, or the risk of error, non-epistemic values are required in science wherever non-epistemic consequences of error should be considered. I use examples from dioxin studies to illustrate how non-epistemic consequences of error can and should be considered in the internal stages of science: choice of methodology, characterization of data, and interpretation of results.
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Decisions based on predictions require decision makers to accept a certain inductive risk.
“Decisions based on such predictions therefore require decision makers to accept a certain inductive risk (Douglas 2000).”
From Explainable AI Isn't Enough! Rethinking Algorithmic Contestability (2026) · cites this paper as Douglas 2000
Cites
Science as Social Knowledge: Values and Objectivity in Scientific Inquiry
Longino, H. E. (1990) · doi:10.1515/9780691209753 · Cited by 3,092 · see citing works
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Science, Values, and the New Demarcation Problem
Resnik, D. B., & Elliott, K. C. (2023) · Journal for General Philosophy of Science · doi:10.1007/s10838-022-09633-2 · Cited by 49 · see citing works
Value-entanglement and the integrity of scientific research
Resnik, D. B., & Elliott, K. C. (2019) · Studies in History and Philosophy of Science Part A · doi:10.1016/j.shpsa.2018.12.011 · Cited by 49 · see citing works
In conversation with Artificial Intelligence: aligning language models with human values
Kasirzadeh, A., & Gabriel, I. (2022) · arXiv · doi:10.48550/arxiv.2209.00731 · Cited by 128 · see citing works
“These decisions are informed, in turn, by how seriously scientists account for the risk of making a mistake in accepting or rejecting a hypothesis, understood against a backdrop of human interests and human needs [30].”
Background“Choice-supportive bias” in science: Explanation and mitigation
Kafaee, M., Marhamati, H., & Gharibzadeh, S. (2021) · Accountability in Research · doi:10.1080/08989621.2021.1872377 · Cited by 12 · see citing works
Why and how to incorporate issues of race/ethnicity and gender in research integrity education
Rasmussen, L. M. (2024) · Accountability in Research · doi:10.1080/08989621.2023.2239145 · Cited by 6 · see citing works
“…assumed to be reliable may not be”); 2) the gathering and characterization of data (“[a] piece of data accepted as sound may be the product of error”), and 3) the interpretation of data (“[a]n interpretation may rely on a selected background assumption that is erroneous” (Douglas 2000, 565).”
InfluentialAre Attributions of Consciousness to AI Chatbots Epistemically Innocent?
Peters, U. (2026) · Minds and Machines · doi:10.1007/s11023-026-09795-8
Explainable AI Isn't Enough! Rethinking Algorithmic Contestability
Freiesleben, T., Meding, K., & König, G. (2026) · arXiv · doi:10.48550/arxiv.2605.16041
- Authors
- Douglas, H.
- Affiliations
- University of Puget Sound (US)
- Corresponding author
- Douglas, Heather
- ORCID
- Douglas, Heather
- Venue
- Philosophy of Science
- Publisher
- Cambridge University Press (CUP)
- Published
- 2000-12
- Volume
- 67
- Issue
- 4
- Pages
- 559-579
- ISSN
- 0031-8248 (ISSN-L)
- 1539-767X
- OpenAlex
- W2075398166
- Cited by
- 885
- References
- 1
- Citations by year
- 2026: 79 · 2025: 112 · 2024: 70 · 2023: 88 · 2022: 89 · 2021: 86 · 2020: 78 · 2019: 44 · 2018: 44 · 2017: 25 · 2016: 21 · 2015: 37 · 2014: 20 · 2013: 21 · 2012: 16
- FWCI
- 18.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Philosophy of Science (publishedVersion)
- PhilSci-Archive (University of Pittsburgh) (acceptedVersion)
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy, Science, and History
- Social Sciences › Sociology and Political Science › Feminist Epistemology and Gender Studies
- Keywords
- inductive risk, non-epistemic values, epistemic value, research methodology, hypothesis selection, data characterization, interpretation of results, value-laden science
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