# Machine Learning and Theory Ladenness -- A Phenomenological Account

arXiv: https://arxiv.org/abs/2409.11277 · HTML: https://mlsanity.com/paper/2409.11277/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 93 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024)
- [Causal Machine Learning: A Survey and Open Problems](https://mlsanity.com/paper/2206.15475/) (2022)
- [The Pragmatic Turn in Explainable Artificial Intelligence (XAI)](https://mlsanity.com/paper/2002.09595/) (2020)
- [Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena](https://mlsanity.com/paper/2206.05487/) (2022)
- [Statistical modeling: the three cultures](https://mlsanity.com/paper/2012.04570/) (2020)
- [Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research](https://mlsanity.com/paper/2301.08525/) (2023)

## Built on by (most cited first, 3 of 3)

- [Three Kinds of AI Ethics](https://mlsanity.com/paper/2503.18842/) (2025)
- [Words as Difference Makers: How Large Language Models Determine Causal Structure in Text](https://mlsanity.com/paper/2606.22430/) (2026)
- [What Can Artificial Intelligence Learn from Medicine? Generative Analogies and Reliable Machine Learning Systems](https://mlsanity.com/paper/2608.18186/) (2026)

## Abstract (arXiv)

We provide an analysis of theory ladenness in machine learning in science, where "theory", that we call "domain theory", refers to the domain knowledge of the scientific discipline where ML is used. By constructing an account of ML models based on a comparison with phenomenological models, we show, against recent trends in philosophy of science, that ML model-building is mostly indifferent to domain theory, even if the model remains theory laden in a weak sense, which we call theory infection. These claims, we argue, have far-reaching consequences for the transferability of ML across scientific disciplines, and shift the priorities of the debate on theory ladenness in ML from descriptive to normative.