On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms
Journal:
arXiv
Published Date:
Feb 13, 2025
Abstract
To create usable and deployable Artificial Intelligence (AI) systems, there
requires a level of assurance in performance under many different conditions.
Many times, deployed machine learning systems will require more classic logic
and reasoning performed through neurosymbolic programs jointly with artificial
neural network sensing. While many prior works have examined the assurance of a
single component of the system solely with either the neural network alone or
entire enterprise systems, very few works have examined the assurance of
integrated neurosymbolic systems. Within this work, we assess the assurance of
end-to-end fully differentiable neurosymbolic systems that are an emerging
method to create data-efficient and more interpretable models. We perform this
investigation using Scallop, an end-to-end neurosymbolic library, across
classification and reasoning tasks in both the image and audio domains. We
assess assurance across adversarial robustness, calibration, user performance
parity, and interpretability of solutions for catching misaligned solutions. We
find end-to-end neurosymbolic methods present unique opportunities for
assurance beyond their data efficiency through our empirical results but not
across the board. We find that this class of neurosymbolic models has higher
assurance in cases where arithmetic operations are defined and where there is
high dimensionality to the input space, where fully neural counterparts
struggle to learn robust reasoning operations. We identify the relationship
between neurosymbolic models' interpretability to catch shortcuts that later
result in increased adversarial vulnerability despite performance parity.
Finally, we find that the promise of data efficiency is typically only in the
case of class imbalanced reasoning problems.
