Journal of Ambient Intelligence and Humanized Computing, Volume 14, Issue 6, Pages 7827-7843 , 01/06/2023

The role of explainable Artificial Intelligence in high-stakes decision-making systems: a systematic review

Bukhoree Sahoh, Anant Choksuriwong

Abstract

A high-stakes event is an extreme risk with a low probability of occurring, but severe consequences (e.g., life-threatening conditions or economic collapse). The accompanying lack of information is a source of high-stress pressure and anxiety for emergency medical services authorities. Deciding on the best proactive plan and action in this environment is a complicated process, which calls for intelligent agents to automatically produce knowledge in the manner of human-like intelligence. Research in high-stakes decision-making systems has increasingly focused on eXplainable Artificial Intelligence (XAI), but recent developments in prediction systems give little prominence to explanations based on human-like intelligence. This work investigates XAI based on cause-and-effect interpretations for supporting high-stakes decisions. We review recent applications in the first aid and medical emergency fields based on three perspectives: available data, desirable knowledge, and the use of intelligence. We identify the limitations of recent AI, and discuss the potential of XAI for dealing with such limitations. We propose an architecture for high-stakes decision-making driven by XAI, and highlight likely future trends and directions.

Document Type

Article

Source Type

Journal

Keywords

Bayesian networksCausal discoveryCausal inferenceCause and effectDeep learningMachine learning

ASJC Subject Area

Computer Science : Computer Science (all)


Bibliography


Sahoh, B., & Choksuriwong, A. (2023). The role of explainable Artificial Intelligence in high-stakes decision-making systems: a systematic review. Journal of Ambient Intelligence and Humanized Computing, 14(6) 7827-7843. doi:10.1007/s12652-023-04594-w

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