Human Research Strategies vs Research Shaped by Algorithms – Why do Humans Need Phenomena and Models?

In this keynote lecture at the Symposium on Digital Transformation of Research (DiTraRe), I will address two questions. First, what fundamental epistemological differences exist between human research strategies and research shaped by algorithms. Second, what are consequences for the objective phrased in the Reflection and Resonance dimension of DiTraRe “to establish trust in research that is shaped by algorithms, [where] it is vital to ensure transparent, reproducible practices and clear communication of findings.”

To explore these questions, I will draw on previous philosophical work that emphasizes the crucial role of descriptions of phenomena and conceptual models in human research strategies (Boon, 2015; 2020a). In contrast, ideas about AI-based scientific research suggest that phenomena and models are redundant once large amounts of data are available (Boon, 2020b).

In this lecture, I will use examples from experimental and empirical (data generating) practices in the natural, engineering and social sciences to explain how humans generate and use data, descriptions of phenomena and conceptual models in their scientific reasoning to arrive at a (scientific) knowledge.

My objective is to provide a deeper (epistemological) understanding of scientific reasoning strategies that align with human cognition and to invite critical reflection by the audience on how this compares to AI-based research.

Mieke Boon bio

Mieke Boon is professor of philosophy of science in practice at the Department of Philosophy of the University of Twente. She holds a PhD in chemical engineering and biotechnology. In 2006, she established a new movement, dubbed the philosophy of science in practice. Her research in this field focuses on epistemology, with particular interest in how human reasoning (i.e., their reasoning strategies) enables the construction of epistemic results (theories, models, concepts and phenomena, laws) that meets stringent epistemic and pragmatic criteria. Topics she has been publishing on are scientific models, representation, scientific instruments, phenomena, paradigms of science, scientific methodology, and epistemological responsibility. Current topics include AI, interdisciplinarity, and science education.

References and recommended reading

Boon, M. (2020a). The role of disciplinary perspectives in an epistemology of scientific models. European journal for philosophy of science, 10(3), 31.

Boon, M. (2015). Contingency and inevitability in science-Instruments, interfaces and the independent world. In Science as it could have been: Discussing the contingent/inevitable aspects of scientific practices. L. Soler and E. Trizio (ed.).  University of Pittsburgh Press: 151-174. 

Boon, M. (2020b). How Scientists Are Brought Back into Science—The Error of Empiricism. In M. Bertolaso, & F. Sterpetti (Eds.), A Critical Reflection on Automated Science: Will Science Remain Human? (Vol. 1, pp. 43-65). (Human Perspectives in Health Sciences and Technology; Vol. 1). Springer. https://doi.org/10.1007/978-3-030-25001-0_4

Boon, M., & Van Baalen, S. (2019). Epistemology for interdisciplinary research–shifting philosophical paradigms of science. European journal for philosophy of science, 9(1), 16. https://doi.org/10.1007/s13194-018-0242-4

Giere, R. (1996) Understanding Scientific Reasoning Wadsworth Publishing

Kwa, C. (2011). Styles of knowing: A new history of science from ancient times to the present. University of Pittsburgh Press.

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Bailer-Jones, D. M. (2009). Scientific Models in Philosophy of Science. Pittsburgh: University of Pittsburgh Press.

Bogen J. and J. Woodward (1988). Saving the phenomena. The Philosophical Review, 97(2): 303-352.

Hacking I. (1983). Representing and Intervening: Introductory Topics in the Philosophy of Natural Science. Cambridge: Cambridge University Press.

Glymour, B. (2000). Data and Phenomena: A Distinctions Reconsidered. Erkenntnis, 52: 29-37.

Massimi M. (2008). Why There are No Ready-Made Phenomena: What Philosophers of Science Should Learn From Kant, Royal Institute of Philosophy Supplement 83(63): 1-35.

Massimi, M. (2011). From data to phenomena: A Kantian stance. Synthese, 182(1): 101-116-71.

McAllister J.W. (1997). Phenomena and patterns in data sets. Erkenntnis, 47, 217-228.

McAllister W. (2011). What do patterns in empirical data tell us about the structure of the world? Synthese, 182(1): 73-87. 

Woodward J.F. (2011). Data and phenomena: a restatement and defense. Synthese, 182(1): 165-179.