When Are Neural Interaction Discoveries Real? Identifiability, Recoverability, and a Pre-Fit Diagnostic
arXiv:2606.08390v1 Announce Type: new Abstract: When a neural time-series model reports that one variable modulates another's effect on a target, is the discovered interaction a property of the data or an artifact of model flexibility? We argue that this is fundamentally a question of identifiability, governed by the geometry of the observed input support rather than by the specific neural architecture. We study the problem in a multiplicative-gating extension of neural additive vector autoregression (GNAVAR), in which source contributions are modulated by other lagged variables. We show that
The proliferation of complex AI models necessitates more rigorous methodologies to ensure their discoveries are reliable and interpretable, driving research into model trustworthiness.
Understanding the identifiability and recoverability of neural network discoveries is crucial for deploying AI in critical applications where trust and explainability are paramount, preventing spurious correlations from being acted upon.
This research provides a pre-fit diagnostic tool that could enhance the reliability of insights derived from neural time-series models before extensive computational resources are committed, improving the efficiency and trustworthiness of AI-driven analysis.
- · AI researchers
- · Data scientists
- · High-stakes AI applications
- · Model interpretability tools
- · Overly flexible neural models
- · Interpretability black boxes
Improved confidence in the discoveries reported by neural time-series models for interaction effects.
Faster development and deployment of reliable AI systems in fields like finance, medicine, and climate science.
Enhanced regulatory and audit frameworks for AI, potentially leading to 'certifiable' insights from machine learning models.
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Read at arXiv cs.LG