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What remains for human scientists when artificial intelligence can formulate hypotheses, conduct experiments, analyze data, and write scientific papers? Perhaps the most important part of science: deciding which questions are worth asking, which findings matter, and what purposes scientific research should ultimately serve.
Especially now, as AI and large language models (LLMs) take on an ever-increasing share of scientific work across many academic disciplines, it becomes all the more clear that it is ultimately human value judgments that give science meaning – and thus value. As a recent Nature editorial emphasizes, science is about more than efficiently producing knowledge. Human wisdom, empathy, and curiosity are central to the scientific enterprise.
“Value-free” science has always been impossible anyway, as has the goal of science that is ‘as value-free as possible.’ Rather, values inevitably inform what we investigate, how we investigate it, and how we deal with uncertainty. AI and LLMs make it increasingly clear that these normative aspects of science may ultimately be the ones that must remain under human control.
Consider the selection of research questions. AI might help us develop new pharmaceuticals more rapidly than ever before. But should we prioritize treatments for diseases affecting millions of people in poorer countries or more profitable therapies for wealthy patients? An algorithm can rank research opportunities according to whatever criteria it is given, but choosing those criteria is itself a normative decision.
Normative judgments also enter seemingly technical decisions. In quantitative management research, for example, evaluating strategies or workplace interventions requires deciding whether productivity, profitability, job satisfaction, or employee well-being should be the primary measure of ‘success’. None of these choices is neutral and not even categorizations within each measure are.
There is also a collective dimension to this issue. In a recent PNAS opinion piece, Michael Hochberg and Peter Thrall warn that AI could reorganize scientific research around the incentives of technology providers, funders, and publishers. Similarly, Lisa Messeri and Molly Crockett caution against scientific monocultures, where AI-driven research privileges particular methods, questions, and perspectives at the expense of others. Yet, to some degree, this is just an AI-driven variant of paradigmatic dominance in the sense of Kuhn – albeit one potentially reinforced at unprecedented speed and scale.
The implication is not that scientists should refrain from using AI or cling to tasks that machines can perform more efficiently. Rather, the growing automation of scientific work makes the explicit reflection and discussion of normative underpinnings more important, not less.
