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Epistemology and Theory of Machine Learning

Location: Geschw.-Scholl-Pl. 1 (A) - A 120

30.05.2025 at 09:15  – 31.05.2025 at 15:30 

Idea & motivation

This is the second edition of the Epistemology and Theory of Machine Learning series started in 2023.

The rapid rise and huge impact of methods in machine learning raises important philosophical questions. There is, in particular, an increasing interest in questions of epistemology: how exactly do machine learning methods contribute to the pursuit of knowledge? Issues under this header include the justification and the fundamental limitations of such methods, their interpretability, and their implications for scientific reasoning in general. Since machine learning algorithms are, in the end, formal procedures, a formally-minded philosophical approach promises to be particularly fruitful for making progress on these issues. Such a study of modern machine learning algorithms can draw from a long tradition of work in formal epistemology and philosophy of science, as well as from work in computer science and the mathematics of machine learning. The aim of this workshop is to discuss epistemological questions of machine learning in this spirit.

This edition is organized by the Emmy Noether junior research group “From Bias to Knowledge: The Epistemology of Machine Learning”, funded by the German Research Foundation (DFG).

Confirmed speakers

Registration

Registration is free but required. You can register here.

Location

LMU München
Geschwister-Scholl-Platz 1
80539

Schedule

Day 1 (Friday 30 May)
09.15 Welcome                                         
09:30 - 10:15 Levin Hornischer
10:15 - 11:00 Lisa Wimmer
11:00 - 11:15 Coffee break
11:15 - 12:00 Michael Herrmann
12:00 - 12:45 Sara Jensen
12:45 - 14:15 Lunch break
14:15 - 15:00 Heather Champion
15:00 - 15:45 Vincent Fortuin
15:45 - 16:00 Coffee break
16:00 - 16:45 Carlos Zednik
18:30 Workshop dinner

 

Day 2 (Saturday 31 May)
09:30 -10:15 Donal Khosrowi                                      
10:15 - 11:00 Moritz Herrmann
11:00 - 11:15 Coffee break
11:15 - 12:00 Luis Lopez
12:00 - 12:45 Frauke Stoll
12:45 - 14:15 Lunch break
14:15 - 15:00 Anders Søgaard
15:00 - 15:45 Silvia Milano

Abstracts

Heather Champion (Western/Tübingen): Beyond conceptual change: towards a mid-level theory of strong novelty for ML-enabled science

Recent philosophical accounts of machine learning (ML)’s impact on science prioritize context-specific views of strong novelty as theoretical or conceptual revision. While it is uncontroversial that conceptual change represents a more general, widely relevant dimension of high impact, I argue that a “mid-level” theory of strong novelty has several upshots. Particularly, it should guide the design of new research projects with ML, including those that might aim at conceptual change. I present novelty desiderata that signal high impact to existing scientific knowledge or research direction. I illustrate these with cases of scientific discovery from various domains, such as economics and astrophysics. Furthermore, I define novelty relative to a discovering collective in contrast to purely psychological (individual) or historical (domain-wide) accounts.

While conceptual change makes broad scientific impact (e.g. concepts structure theory), local belief revisions make deep impact—either enlarging existing theory or changing the direction of research. First, eliminating deep ignorance generates awareness of useful patterns, evidence, or hypotheses. Surprising outcomes change an idea’s expected utility, while reducing utility “blindness” steers research when prior uncertainty is very high. Meanwhile, learning outcomes achieved with some independence of local theory that demarks or explains phenomena afford strong novelty: local theory captures the kind of prior information regarding phenomena that diminishes scientific impact. Thus, to fully appreciate the ways that ML advances science, philosophical consideration of novelty and ML must move beyond conceptual change.

Vincent Fortuin (TUM & Helmholz AI): Philosophical Reflections on Bayesian Deep Learning

Bayesian inference has long been celebrated as a normative model of rational belief updating, grounded in foundational work by de Finetti, Cox, Savage, and Wald. These philosophical justifications paint Bayesianism as uniquely suited to represent uncertainty, incorporate prior knowledge, and guide rational action under uncertainty. However, when Bayesian methods are brought into the domain of deep learning, these justifications come under strain. Priors are often chosen for computational convenience rather than sincere epistemic commitment, and inference is typically approximate, relying on techniques like variational inference or Monte Carlo sampling that may diverge significantly from ideal Bayesian reasoning.

In this talk, I explore the philosophical tensions that arise when Bayesian principles are applied in large-scale, high-dimensional machine learning settings. I argue that while traditional justifications falter under practical constraints, Bayesian deep learning can be reframed within a more pragmatic perspective. I consider several paths forward, including bounded rationality, engineering pragmatism, and the idea of a computational epistemology that accommodates approximation and heuristic reasoning. Rather than abandoning Bayesianism, we may need to reinterpret its role—not as a strict epistemic ideal, but as a guiding framework for navigating uncertainty in complex, computationally limited systems.

Moritz Herrmann (LMU/IBE): Machine Learning as an Empirical Science: Conceptual Approaches and Practical Insights

Treating machine learning as an empirical science grounded in experimental exploration and evaluation raises specific epistemic challenges. How can theoretical assumptions be translated into experimental designs that meaningfully address scientific questions? How should we account for the multiplicity of design choices and other sources of uncertainty? And in what sense can we generalize experimental findings? In this talk, I reflect on these questions through several experimental studies in both supervised and unsupervised learning. I focus in particular on the impact of design and analysis choices, and on the challenges of generalizing results from method comparison experiments. I argue that a narrow understanding of experimental research risks limiting the development of machine learning as a scientific field. To move forward, we need a broader perspective that embraces diverse types of research contributions and epistemic goals – but also accepts inconclusiveness as a valid and sometimes unavoidable outcome.

Sara Jensen (Oslo): The Underdetermination of Representational Content in DNNs

There is widespread hope of using ML models to make new scientific discoveries. As part of this, much effort is being put into establishing methods for interpreting the learned basis vectors in the latent spaces of deep neural networks (DNNs), motivated by the belief that the networks implicitly learn scientifically relevant representations and concepts from the data. By studying these learned representations, we may learn about new dependencies and structures in nature. There is disagreement regarding how concepts are represented in the hidden layers, specifically whether they are localised or distributed across nodes, and whether they are linear or non-linear. I argue that for distributed representations, the conceptual content of the representations will often be underdetermined. This happens for sets of variables which are defined in terms of each other, such as volume, temperature and pressure. This shows a crucial difference between classical scientific representations and representations in DNNs, which will likely have implications for the hope of extracting and learning new scientific concepts and dependencies from such models.

Donal Khosrowi (Hannover): Can Generative AI Produce Novel Evidence?

Researchers across the sciences increasingly explore the use of generative AI (GenAI) systems for various inferential and practical purposes, such as for drug and materials discovery and synthesis, or for reconstructing destroyed manuscripts and artifacts in the historical sciences. This paper explores a novel epistemological question: can GenAI systems generate evidence that provides genuinely new knowledge about the world or can they only produce hypotheses that we might seek evidence for? Exploring responses to this question, the paper argues that 1) GenAI outputs can at least be understood as higher-order evidence (Parker 2022) and 2) may also constitute de novo synthetic evidence. We explore the wider ramifications of this latter thesis and offer additional strictures on when synthetic evidence can be strong evidence for claims about the world.

Frauke Stoll (Dortmund): Empirical and Theoretical Links: Rethinking the Role of DNNs in Scientific Understanding

What role can deep neural networks (DNNs) play in advancing scientific understanding? In this talk, I argue that DNNs can support the early stages of understanding by uncovering empirical patterns and regularities, but that their contribution to deeper explanatory understanding depends on overcoming two distinct kinds of link uncertainty. Drawing on Emily Sullivan’s account, empirical link uncertainty refers to the degree of evidence connecting a model to its target phenomenon. But as the case of the Rydberg formula illustrates, empirical connection alone is insufficient: explanatory understanding also depends on theoretical link certainty—the integration of models into a broader theoretical framework. DNNs, even when supplemented by explainable AI (XAI) methods, largely operate at the instrumental and descriptive levels, clarifying what patterns are present and sometimes how they emerge, but not why they hold. This positions DNNs as akin to phenomenological models, which capture surface regularities without revealing underlying mechanisms. Yet unlike phenomenological models, DNNs are doubly opaque: they obscure both mechanisms and the regularities themselves, lacking an interpretable mathematical structure.I propose that opacity in DNNs should be understood hierarchically—as what-, how-, and why-opacity—each posing distinct challenges for understanding. While empirical link certainty facilitates progress across these levels, only theoretical embedding can transform DNN outputs from descriptive tools into sources of explanatory understanding. Situating DNNs within the broader modeling literature thus clarifies both their potential and their limitations as instruments of scientific inquiry.

Practical information

The workshop will take place in the main building of LMU Munich (Geschwister-Scholl-Platz 1), in room A 120.

You can find a map of the building with room A 120 marked at
https://www.lmu.de/raumfinder/#/building/bw0000/map?room=000001209_

How to get there by public transport

Train: Arrival at München Hauptbahnhof (Munich Main Station) or München Ostbahnhof (Munich East Station), then take the S-Bahn to Marienplatz and from there the U3 or U6 to stop Universität. To plan your travel, visit https://mvv-muenchen.de .

S-Bahn (City train): All lines to Marienplatz, then U-Bahn.