Propagation of~Interval Belief Structures and~Imprecise Copulas for~Neural Network Verification
arXiv:2606.30105v1 Announce Type: new Abstract: Quantitative verification of neural networks requires reasoning about probabilities under substantial uncertainty in both input distributions and their dependence structure. In realistic settings, this information is often only partially specified, and assuming precise probabilistic models can lead to unreliable results. We propose a sound framework for quantitative verification under imprecise probabilistic information, combining interval belief structures to represent marginal uncertainty with imprecise copulas to model uncertain dependence. We
The increasing deployment of neural networks in critical applications necessitates robust verification methods, prompting research into handling real-world uncertainties in their operation.
This development addresses a fundamental challenge in AI adoption by providing a framework for reliable neural network verification under imprecise probabilistic information, enhancing trust and safety.
The ability to formally verify neural networks despite data uncertainties will allow for broader deployment in sensitive environments where probabilistic precision was previously a barrier.
- · AI safety researchers
- · High-stakes AI deployment sectors
- · Regulators of AI
- · Companies using AI for critical infrastructure
- · Developers of unverified AI systems
- · Systems relying on highly precise probabilistic models
Increased confidence in AI system reliability will drive wider adoption in regulated industries.
New standards for AI verification that incorporate imprecise probability will likely emerge.
The development of 'imprecise AI' could become a distinct field, focusing on robust decision-making under deep uncertainty.
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Read at arXiv cs.AI