Machine Learning and the Future of Realism
Not on the map. Why?
Abstract (arXiv)
The preceding three decades have seen the emergence, rise, and proliferation of machine learning (ML). From half-recognised beginnings in perceptrons, neural nets, and decision trees, algorithms that extract correlations (that is, patterns) from a set of data points have broken free from their origin in computational cognition to embrace all forms of problem solving, from voice recognition to medical diagnosis to automated scientific research and driverless cars, and it is now widely opined that the real industrial revolution lies less in mobile phone and similar than in the maturation and universal application of ML. Among the consequences just might be the triumph of anti-realism over realism.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Apr 2017.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 1; 2025: 1.
4 papers.
- 2022Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomenacited by 21
- 2022Deep Learning Opacity in Scientific Discoverycited by 9
- 2024Targeted Learning for Variable Importancecited by 0
- 2025Bridging Philosophy and Machine Learning: A Structuralist Framework for Classifying Neural Network Representationscited by 0
- 2025Bridging Philosophy and Machine Learning: A Structuralist Framework for Classifying Neural Network Representations23 Nov 2025
- 2024Targeted Learning for Variable Importance4 Nov 2024
- 2022Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena11 Jun 2022
- 2022Deep Learning Opacity in Scientific Discovery1 Jun 2022