ExplainabilityTransparencyTrustworthinesscs.LG
A Critical Review of Inductive Logic Programming Techniques for Explainable AI
Zheng Zhang, Liangliang Xu, Levent Yilmaz, Bo Liu
December 31, 2021
IEEE Transactions on Neural Networks and Learning Systems
24 citations
Key Takeaways
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Logic-based AI offers a path to transparency: Inductive Logic Programming uses intuitive, rule-based reasoning to create explanations that humans can understand, providing an alternative to "black box" machine learning systems that give results without showing their reasoning.
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Current ILP systems struggle with real-world complexity: Existing ILP methods face significant practical limitations, including difficulty handling large solution spaces and sensitivity to noisy or imperfect data—challenges that must be resolved before these systems can be reliably deployed in practice.
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Hybrid approaches combining symbolic and neural methods show promise: The paper highlights that combining ILP with statistical relational learning and neural-symbolic algorithms offers complementary strengths, suggesting that the future of explainable AI may lie in integrating logic-based and modern machine learning approaches rather than choosing one over the other.
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Explainability is critical for building trust in AI: The paper emphasizes that the opacity of modern machine learning remains a barrier to AI adoption, and developing self-explanatory systems through methods like ILP is essential for instilling confidence in high-stakes applications where users need to understand how decisions are made.
Abstract
Despite recent advances in modern machine learning algorithms, the opaqueness of their underlying mechanisms continues to be an obstacle in adoption. To instill confidence and trust in artificial intelligence systems, Explainable Artificial Intelligence has emerged as a response to improving modern machine learning algorithms' explainability. Inductive Logic Programming (ILP), a subfield of symbolic artificial intelligence, plays a promising role in generating interpretable explanations because of its intuitive logic-driven framework. ILP effectively leverages abductive reasoning to generate explainable first-order clausal theories from examples and background knowledge. However, several challenges in developing methods inspired by ILP need to be addressed for their successful application in practice. For example, existing ILP systems often have a vast solution space, and the induced solutions are very sensitive to noises and disturbances. This survey paper summarizes the recent advances in ILP and a discussion of statistical relational learning and neural-symbolic algorithms, which offer synergistic views to ILP. Following a critical review of the recent advances, we delineate observed challenges and highlight potential avenues of further ILP-motivated research toward developing self-explanatory artificial intelligence systems.