Integrating Knowledge Representation and Logical Inference for Explainable Artificial Intelligence in High-Stakes Decision-Making
Keywords:
Explainable AI (XAI), Knowledge Representation, Logical Inference, High-Stakes Decisions, Symbolic AI, Decision TransparencySynopsis
High-stakes decision-making applications (e.g., healthcare, autonomous systems, legal judgement) require explainable artificial intelligence (XAI) that is both transparent and reliable. This paper explores how Knowledge Representation (KR) and Logical Inference (LI) integrate to support explainability in AI systems. We evaluate current methods, identify integration challenges, demonstrate experimental results using benchmark datasets, and discuss future directions. Results show that hybrid KR + LI approaches improve explanation quality and decision transparency compared to black-box models.
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