Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms

Luke E. Richards, Jessie Yaros, Jasen Babcock, Coung Ly, Robin Cosbey, Timothy Doster, Cynthia Matuszek

TL;DR

This paper investigates whether end-to-end differentiable neurosymbolic reasoning (NESY) can provide assurance comparable to or better than fully neural systems. Using Scallop, the authors evaluate NESY across MNIST, CIFAR-10, LEAF-ID, Pathfinder, and Common Voice Clips, focusing on interpretability, calibration, robustness, and user parity. They find that NESY offers assurance advantages in logic-like and high-dimensional settings, but gains are not universal and reliance on symbolic shortcuts in perception-grounding can increase adversarial vulnerability; data efficiency benefits are task-dependent. The work highlights both the promise and current limitations of differentiable neurosymbolic reasoning for assurance and points to interpretability-driven safeguards as a crucial area for future research.

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.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms

TL;DR

This paper investigates whether end-to-end differentiable neurosymbolic reasoning (NESY) can provide assurance comparable to or better than fully neural systems. Using Scallop, the authors evaluate NESY across MNIST, CIFAR-10, LEAF-ID, Pathfinder, and Common Voice Clips, focusing on interpretability, calibration, robustness, and user parity. They find that NESY offers assurance advantages in logic-like and high-dimensional settings, but gains are not universal and reliance on symbolic shortcuts in perception-grounding can increase adversarial vulnerability; data efficiency benefits are task-dependent. The work highlights both the promise and current limitations of differentiable neurosymbolic reasoning for assurance and points to interpretability-driven safeguards as a crucial area for future research.

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.

Paper Structure

This paper contains 30 sections, 2 figures, 16 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Background
  4. Differentiable Neurosymbolic Reasoning
  5. Measurements of Assurance
  6. Experiments
  7. MNIST Logic Tasks: Multi-Image Logic Tasks
  8. CIFAR-10: Knowledge-base Reasoning for Classification
  9. LEAF-ID: Grounded Attribute Reasoning for Classification
  10. Pathfinder: Pixel-Level Reasoning
  11. Speech Word Classification: Common Voice Clips
  12. Results
  13. MNIST Logic Tasks: Sum Results
  14. MNIST Logic Tasks: How Many 3 or 4 Results
  15. CIFAR-10 Results
  16. ...and 15 more sections

Figures (2)

  • Figure 1: Visualizations of the two paradigms examined and the assurance metrics measured.
  • Figure 2: Internals of the Pathfinder symbolic models on the input (left), the joint neural facts imposed in the same image space to show overlap, and then separated dot and path facts with probability as the intensity of color. We see that a circuit is formed for all examples in the symbolic space that forms a shortcut for performing classification that is not grounded in actual perception.