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Explaining solutions from Linear Programs (LPs) is challenging. This study shows how encoding LPs as neural networks enables effective explanation methods, improving AI interpretability.

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Area of Science:

  • Artificial Intelligence
  • Operations Research
  • Machine Learning

Background:

  • Linear Programs (LPs) are fundamental to AI and optimization.
  • Existing explainable AI (XAI) methods primarily focus on deep learning, neglecting LPs.
  • LPs, despite being white-box, present challenges in understanding input-output relationships.

Purpose of the Study:

  • To develop methods for explaining the solutions of Linear Programs.
  • To adapt existing attribution methods for explaining LP outputs.
  • To enhance the interpretability of AI systems that utilize LPs.

Main Methods:

  • Encoding Linear Programs into a neural network format.
  • Adapting attribution methods like Saliency and LIME for neural LP encodings.
  • Evaluating explanation methods on various LPs, including large-scale instances (10k dimensions).

Main Results:

  • Neural encoding successfully enables the application of attribution methods to LPs.
  • The proposed approach demonstrates the explainability of LP solutions.
  • Saliency and LIME show similar performance at low perturbation levels.

Conclusions:

  • Linear Programs can and should be explained for better AI transparency.
  • Representing LPs as neural networks is a viable strategy for enhancing their explainability.
  • This work bridges the gap between optimization and explainable AI.