The Role of Values in Science

Core Idea

Science is not purely objective; non-epistemic values—cultural, social, political, and personal—influence theory choice, funding priorities, research agendas, and which questions scientists pursue. Understanding the pervasive role of values is crucial for evaluating science's objectivity and for responsible scientific practice in society.

Explainer

Your introduction to philosophy of science gave you the basic picture: science pursues objective knowledge through empirical testing, peer review, and the self-correcting dynamics of the scientific community. This picture is broadly correct — science is our best-developed method for producing reliable knowledge about the world. But it carries with it a picture of science as value-free that the philosophy of science has progressively complicated. Understanding how values enter science does not undermine it; it makes visible the conditions under which science succeeds or fails to be trustworthy.

The first distinction to draw is between epistemic values and non-epistemic values. Epistemic values are internal to science's aims: simplicity, accuracy, consistency, explanatory breadth, and fruitfulness. Scientists rightly prefer simpler theories, more accurate predictions, and more unified explanations — these preferences are constitutive of good scientific reasoning. Non-epistemic values — moral, political, social, aesthetic, economic — come from outside science's core epistemic aims. The traditional ideal of value-free science means keeping non-epistemic values out of theory assessment. The question is whether this ideal is achievable.

Several doors let non-epistemic values in. Funding and agenda-setting: what gets studied depends on what gets funded, and funding flows toward socially and economically valued problems. Pharmaceutical companies, defense agencies, and governments set priorities. Underdetermination: your introduction to philosophy of science should have touched on the fact that evidence often does not uniquely determine which theory is correct — multiple theories can fit the same data. In those gaps, scientists use background assumptions and contextual values to choose between rivals. Helen Longino argues that even the standards of what counts as adequate evidence are shaped by background assumptions that reflect social values. Framing and categorization: how researchers describe phenomena — which groups get compared, which variables get measured, how pathology is defined — embeds value judgments into the data itself.

Philosopher Heather Douglas draws a crucial distinction between direct and indirect roles for values. Values play a direct role when they determine whether evidence supports a hypothesis — as when a researcher disregards contradictory evidence for political reasons. This is illegitimate. But values legitimately play an *indirect* role in assessing what kinds of errors are acceptable. In high-stakes contexts (drug safety, environmental regulation, climate policy), how much risk of false positives or false negatives we tolerate depends on what the consequences of each error are — a fundamentally value-laden question. A study that concludes a chemical is safe tolerates a certain false-negative rate; that tolerance reflects value choices about whose interests matter.

The upshot is not relativism — not the claim that science is merely politics by another name, or that one value-laden account is as good as any other. Science's intersubjective methods, replication requirements, and empirical constraints give it genuine epistemic authority that pure opinion lacks. But recognizing the pervasive presence of values makes transparency and democratic accountability more important, not less. Science done well makes its value commitments explicit, subjects them to scrutiny, and distinguishes them from empirical claims. Recognizing value-ladenness is the prerequisite for doing more honest science.