arXiv:2606.02632v1 Announce Type: cross Abstract: Modern Machine Learning (ML) and Artificial Intelligence (AI) models, especially large language models (LLMs), are increasingly used to generate scientific hypotheses and mechanistic explanations from observational data. This position paper argues that in the high-dimensional proxy regimes where modern ML excels, mechanistic learning is generically underdetermined: many incompatible mechanisms induce essentially the same observational relationships on the support of the data, so predictive success and coherent explanations are insufficient evid
The proliferation of complex AI models, particularly LLMs, in scientific research necessitates a critical examination of their methodological validity and interpretive limitations.
This paper challenges the prevailing assumption that predictive success in ML automatically equates to valid scientific discovery, urging a focus on underlying structural identification instead of opaque 'black box' models.
The recommendation shifts emphasis in AI-driven scientific discovery from purely predictive performance to a deeper understanding of latent mechanisms, potentially altering future research methodologies and funding priorities.
- · Explainable AI researchers
- · Fundamental science
- · Causal inference practitioners
- · Purely 'black box' AI model developers
- · Hypothesis generation based solely on correlations
- · Scientific fields overly reliant on opaque AI predictions
Increased scrutiny and demand for interpretable AI models in scientific applications.
A re-evaluation of 'AI for science' benchmarks to include mechanistic understanding rather than just predictive accuracy.
Potential for a new generation of AI tools specifically designed for identifying underlying structures and causal relationships in complex systems.
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Read at arXiv cs.LG