Comparing CKA with AI

Yuanyuan Song (), Richard T. Watson () and Xia Zhao ()

Chapter 2 in Causal Knowledge Analytics, 2025, pp 10-18 from Edward Elgar Publishing

Abstract: Advancements in artificial intelligence have spurred the development of tools for extracting knowledge and analyzing scholarly literature. However, despite these technological strides, AI still faces inherent limitations. IBM's attempt with Watson Health exemplifies the significant challenges with machine-based knowledge recognition and its insufficient accuracy. In addition, the development and accurate calculation of scientific metrics remain beyond the current capabilities of AI. Metrics are crucial to science, from monitoring patient health and treatment efficiency in medical fields to evaluating concepts in social sciences. Causal knowledge analytics is distinguished from the existing efforts by focusing on developing and calculating metrics of causal model networks. This chapter discusses the challenges with knowledge recognition, provides a summary of existing AI tools, and presents how causal knowledge analytics differs from other work.

Keywords: Artificial intelligence; Causal knowledge analytics; Knowledge recognition; Literature review; Metrics (search for similar items in EconPapers)
Date: 2025
ISBN: 9781035353149
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