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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.
This post is provided by Konstantin Hondros, post-doctoral researcher at University of Duisburg-Essen in the DFG-funded research project “Organizing Creativity under Regulatory Uncertainty: Alternative Approaches to Intellectual Property”.
Though “alternative” (both as an adjective and a noun) has widespread meaning in contemporary society, it is not generally clear, what constitutes and conveys something to be (an) “alternative”. This blogpost’s goal is to offer a more nuanced understanding of the concept and ask how this can guide the use of “alternative” as an analytical lens. To begin with, I give an etymological account, then I look at “alternatives” in philosophy and its significance for epistemology, finally, I describe how social sciences make use of “alternative” in an evaluative manner. While from a philosophical perspective, “alternative” is rather a logical operator, in the social sciences “alternative” evaluates institutions, practices, or beliefs. This evaluative use can be either positive and empowering, ambivalent and skeptical, or even negative and destabilizing. I argue that it is this umbrella-term’s multi-facetted and evaluative nature that makes it analytically fruitful for social sciences.
I thus develop the concept of alternative mainly for practical reasons. Our recently kicked-off DFG-funded project Organizing Creativity under Regulatory Uncertainty: Alternative Approaches to Intellectual Property pursues “alternatives” empirically. We take a closer look at how creative processes unfold when intellectual property (IP) is approached “alternatively” and what obstacles and uncertainties these processes encounter. We compare alternatives to the IP-regulations copyright and patent law with case studies from the music economy and the pharmaceutical industries. Differentiating “ alternatives” will inform our methodological and analytical proceeding as it will give as clearer picture of what we are actually dealing with empirically.
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