ENACT. IA et transformations de la découverte scientifique. Une analyse épistémologique. . transforme la découverte How AI reshapes scientific discovery – an epistemological analysis

Offre de thèse

Date limite de candidature

27-04-2025

Date de début de contrat

01-10-2025

Directeur de thèse

IMBERT Cyrille

Encadrement

Co-supervision: Cyrille Imbert (AHP, CNRS, Université de Lorraine) Claus Beisbart (Institute of Philosophy at the University of Bern)

Type de contrat

Concours pour un contrat doctoral

Candidater à cette offre

école doctorale

SLTC - SOCIETES, LANGAGES, TEMPS, CONNAISSANCES

équipe

contexte

This PhD offer is provided by the ENACT AI Cluster and its partners. Find all ENACT PhD offers and actions on https://cluster-ia-enact.ai/.' Archives Poincaré (Université de Lorraine, CNRS, Nancy-Strasbourg, France) The Archives Poincaré is a research institute UMR 7117 that is affiliated with the Université de Lorraine and CNRS (National Institute for Scientific Research), and it benefits from their joint support in terms of hiring academics, facilitating staff, and financial support. The IA PhD fellowships are in the continuity of the research carried out at Poincaré Archives for decades and how it will extended in the coming years. Information about the institute, its members, and salient activities may be found on its site https://poincare.univ-lorraine.fr/fr/axes. The Poincaré Archives are recognized in France and internationally for analysis by philosophers of mathematical and scientific practices in direct dialogue with science, its practices, and the new schemes of scientific reasoning. The following orientations are of particular relevance in the context of research IA research. - Analysis of theoretical and applied mathematics and their mutations - Epistemological analysis is anchored in practices, particularly mathematics and computational science. - Studies related to computer science (codes, concepts, history, digital humanities) - Study of scientific-technological mutations and their social impact (one health medicine, big data, human-machine interactions) - Ethical and political analysis of the transformations of human action: epistemic and digital democracy, social epistemology, climate ethics, decision support Research in the philosophy of science and mathematics, particularly computational science and IA, benefits from strong and regular interactions with the network of other major French or international institutions working on these questions. They typically comprise collaborative work, joint seminars, co-organized workshops and conferences, and cosupervised doctoral students. The Poincaré Archives also has a tradition of strong interactions with scientific fields. The University de Lorraine and the local institutes belonging to national research institutions (such as CNRS, INRIA, INRA, INSERM, GeorgiaTech, or AgroParisTech) provide a rich context for this purpose on fields related to physics, energy science, agriculture, computer science, or health, with various funding opportunities for interdisciplinary research, in particular in the framework of the https://www.univ-lorraine.fr/lue/ The Ph.D. candidates are expected to make the best of this appropriate research environment and to contribute to developing interactions with relevant partners in relation to the philosophy of IA. Institute of Philosophy at the University of Bern The Institute of Philosophy at the University of Bern is one of the largest philosophy departments in Switzerland. It consists of up to 35 researchers and employees. In addition, the Institute is pleased to be welcoming many world-renowned philosophers each semester, as guests to a considerable number of international workshops, lecture series, and courses organized by the Institute. The Institute of Philosophy is subdivided into three departments, the department for logic and theoretical philosophy, the department for practical philosophy and the department for history of philosophy, which are supported by a shared administrative office. The Logic and Theoretical Philosophy division deals in teaching and research, systematically and in part historically, with problems in the areas of logic, metaphysics, epistemology, ontology, philosophy of mind, theory of action, philosophy of language, semantics, and philosophy of mathematics. In addition, in Bern we also focus on the theory and philosophy of natural sciences, and the history of the theory of natural sciences. Research working on subjects related to the philosophy and epistemology of AI involve Claus Beisbart, Dr. Tim Räz Julie Jebeile, or Vincent Lam. Recent or ongoing projects in theoretical philosophy include: - Extending the Scope of Causal Realism - Climate Change Adaptation through the Feminist Kaleidoscope - Ethical Considerations of the Relationships and Interactions between Science, Policy and the Media during the COVID-19 pandemic (ESPRIM) , MCID ECRG_03 - Ethik der Infektionskrankheiten - Improving Interpretability. Philosophy of Science Perspectives on Machine Learning - Explaining Human Nature - The Epistemology of Climate Change - The Rationales and Risks of Systematization: A Pragmatic Account of When to Systematize Thought More details available on https://www.philosophie.unibe.ch/index_eng.html.

spécialité

Philosophie

laboratoire

AHP-PReST - Archives Henri Poincaré - Philosophie et Recherches sur les Sciences et les Technologies

Mots clés

Contexte de découverte, Théories Scientifiques., Imagination, Créativité, Epistémologie, IA Generative

Détail de l'offre

The last few years have seen striking examples of how AI can aid scientific discovery (e.g., Duede 2023, Wang et al. 2023). For instance, AI is used to make drug discovery more efficient (Sellwood et al. 20218), and mathematicians have used AI to develop hypotheses about properties of knots (Davies et al. 2021, see Duede 2023). It is expected that, in the next few years, advances in AI will further boost scientific discovery.
However, we still lack a deeper understanding of how AI can aid discovery or even make discoveries. Is AI's strength mainly in providing new hypotheses? If so, can it move to new levels of description and introduce new concepts, or does it only recombine human ideas? Can it also discover new things or phenomena? If so, how exactly does it do so? These questions ask for a more profound philosophical analysis of discovery involving AI (AI-aided discovery, for short).
Interestingly, in philosophy, discovery has often been put aside. Philosophers of science have concentrated on the justification of theories and left discovery to human creativity (see Popper 1934 for a famous example). Still, some philosophers have tried to trace “patterns of discovery” (as the title of Hanson 1958 has it). For instance, they have put constraints on new theories, e.g., that they are not ad hoc or that they contain successful predecessor theories as limiting cases. Another strand of research has explored the power of models and metaphors to produce fresh perspectives on phenomena (Kuhn 1979, Montuschi).
The proposed PhD project will pioneer the systematic philosophical investigation of AI discovery. It will connect the existing philosophical research literature about scientific discovery to recent examples of AI discovery to answer questions of the following kind:
· How can AI specifically improve scientific discovery?
· In which fields is AI-aided discovery most promising, and why?
· What types of scientific items may be discovered with AI (correlations, general laws, ceteris paribus laws, invariants, causal relations, entities, explanatory patterns, mechanisms, etc.)?
· What role does AI have in discovery? Can it be said to discover things by itself? And then, to what extent do such AI-based discoveries still qualify as intentional? How does AI support humans in discovery?
· How can the power of AI to boost discovery be explained?
· Does AI favor spurious discoveries, and if so, what types?
· Does the opacity of AI impede the exploitation of AI-aided discovery?
· In which directions does AI-aided discovery steer science? Do specific epistemic modes or features of AI methods (e.g., opacity) have an impact on the selection of things that will be discovered?
· How can AI illuminate human scientific discovery (in the same way in which AI is supposed to help understand human cognition)?
· How do AI-based methods compare to other data-intensive methods wrt scientific discovery
The PhD project will select some of these questions and answer them. We anticipate the following stages:
- Survey cases in which AI has been said to foster discovery.
- Analyze the existing philosophical literature about discovery.
- Select cases for case studies and investigate the role of AI in the related discoveries.
- Bring together the findings from the case studies from scientific practice and the philosophical literature.
- Draw consequences for our understanding of science.

Keywords

Context of discovery, Scientific theories, imagination, creativity, epistemology, Generative AI

Subject details

The last few years have seen striking examples of how AI can aid scientific discovery (e.g., Duede 2023, Wang et al. 2023). For instance, AI is used to make drug discovery more efficient (Sellwood et al. 20218), and mathematicians have used AI to develop hypotheses about properties of knots (Davies et al. 2021, see Duede 2023). It is expected that, in the next few years, advances in AI will further boost scientific discovery. However, we still lack a deeper understanding of how AI can aid discovery or even make discoveries. Is AI's strength mainly in providing new hypotheses? If so, can it move to new levels of description and introduce new concepts, or does it only recombine human ideas? Can it also discover new things or phenomena? If so, how exactly does it do so? These questions ask for a more profound philosophical analysis of discovery involving AI (AI-aided discovery, for short). Interestingly, in philosophy, discovery has often been put aside. Philosophers of science have concentrated on the justification of theories and left discovery to human creativity (see Popper 1934 for a famous example). Still, some philosophers have tried to trace “patterns of discovery” (as the title of Hanson 1958 has it). For instance, they have put constraints on new theories, e.g., that they are not ad hoc or that they contain successful predecessor theories as limiting cases. Another strand of research has explored the power of models and metaphors to produce fresh perspectives on phenomena (Kuhn 1979, Montuschi). The proposed PhD project will pioneer the systematic philosophical investigation of AI discovery. It will connect the existing philosophical research literature about scientific discovery to recent examples of AI discovery to answer questions of the following kind: · How can AI specifically improve scientific discovery? · In which fields is AI-aided discovery most promising, and why? · What types of scientific items may be discovered with AI (correlations, general laws, ceteris paribus laws, invariants, causal relations, entities, explanatory patterns, mechanisms, etc.)? · What role does AI have in discovery? Can it be said to discover things by itself? And then, to what extent do such AI-based discoveries still qualify as intentional? How does AI support humans in discovery? · How can the power of AI to boost discovery be explained? · Does AI favor spurious discoveries, and if so, what types? · Does the opacity of AI impede the exploitation of AI-aided discovery? · In which directions does AI-aided discovery steer science? Do specific epistemic modes or features of AI methods (e.g., opacity) have an impact on the selection of things that will be discovered? · How can AI illuminate human scientific discovery (in the same way in which AI is supposed to help understand human cognition)? · How do AI-based methods compare to other data-intensive methods wrt scientific discovery The PhD project will select some of these questions and answer them. We anticipate the following stages: - Survey cases in which AI has been said to foster discovery. - Analyze the existing philosophical literature about discovery. - Select cases for case studies and investigate the role of AI in the related discoveries. - Bring together the findings from the case studies from scientific practice and the philosophical literature. - Draw consequences for our understanding of science.

Profil du candidat

- The candidate is expected to have a Master's degree in philosophy of science, philosophy, or epistemology or to be about to complete this degree. By default, his/her curriculum should provide strong evidence of his/her ability to engage in a philosophical analysis of formal and scientific methods and, in particular, the scientific uses of AI. Strong evidence of excellent writing skills is particularly expected.

- The candidate should have a sufficient understanding of AI methods or a demonstrated ability to quickly acquire relevant and sufficient knowledge about these methods and their application in some scientific fields. Typically, the candidate may have some training or relevant courses in AI methods, an advanced scientific education such as the possession of a Bachelor of Science in a relevant field, or academic evidence that the candidate is able to develop philosophical arguments relying on information about AI methods.

- The candidate should have advanced teamwork skills and be prepared to develop regular interactions in two research sites in order to make the best of his/her joint philosophical research environment in Nancy (France) and Bern (Switzerland).

- The candidate is expected to have strong interaction and organizational skills in order to engage in exchanges with relevant scientists, philosophers in the larger international philosophy of AI community, especially in Europe, and philosophers who have relevant expertise but are not specialized in AI.

Candidate profile

Référence biblio

Davies, Alex, et al. 2021. “Advancing Mathematics by Guiding Human Intuition with AI”. Nature 600 (7887):70–74.
Duede, Eamon (2023). Deep Learning Opacity in Scientific Discovery. Philosophy of Science 90 (5):1089 - 1099.

Hanson, Norwood Russell (1958). Patterns of discovery. Cambridge [Eng.]: University Press.

Kuhn, Thomas S. 'Metaphor in science.' Metaphor and thought 2 (1979): 533-542.

Montuschi, Eleonora (2000). Metaphor in science. In W. Newton-Smith, A companion to the philosophy of science. Malden, Mass.: Blackwell. pp. 277-282.

Popper, Karl R., Logik der Forschung, Springer, Wien 1934

Sellwood, Matthew A., et al. 'Artificial intelligence in drug discovery.' Future medicinal chemistry 10.17 (2018): 2025-2028.

Wang, H., Fu, T., Du, Y. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023). https://doi.org/10.1038/s41586-023-06221-2