Abstract
A high-stakes decision requires deep thought to understand the complex factors that stop a situation from becoming worse. Such decisions are carried out under high pressure, with a lack of information, and in limited time. This research applies Causal Artificial Intelligence to high-stakes decisions, aiming to encode causal assumptions based on human-like intelligence, and thereby produce interpretable and argumentative knowledge. We develop a Causal Bayesian Networks model based on causal science using d-separation and do-operations to discover the causal graph aligned with cognitive understanding. Causal odd ratios are used to measure the causal assumptions integrated with the real-world data to prove the proposed causal model compatibility. Causal effect relationships in the model are verified based on causal P-values and causal confident intervals and approved less than 1% by random chance. It shows that the causal model can encode cognitive understanding as precise, robust relationships. The concept of model design allows software agents to imitate human intelligence by inferring potential knowledge and be employed in high-stakes decision applications.
Highlights
Critical events are unexpected situations that severely affect citizens, infrastructure, and government
This research used Causal AI for high-stakes decisionmaking by utilizing causal science to encode human-like intelligence
Causal encoding based on d-separation and dooperation was applied to model causal assumptions as represented by Causal Bayesian Networks (CBNs) with Causal OR, Causal P-Value, and Causal CI used to discover causal effects by measuring the commonsense behind a graph
Summary
Critical events are unexpected situations that severely affect citizens (e.g., by causing serious injury or death), infrastructure (e.g., via transportation damage or communications failure), and government (e.g., with economic crises or financial loss). Formosa [6] proposed an approach for traffic conflicts using proactive safety management strategies, while Anbarasan et al [7] introduced a technique for highstakes events during flood disasters Both support highperformance accuracy for better decision-making, but current deep learning focuses on detection and explanation performance rather than on supporting high-stakes decisions. Causal AI lets machine learning describe the cognitive reasons for predicted output based on human-like interpretations [9] It aims to produce reasons for "Why" and "How" events happen given current evidence regardless of outcomes, and so synthesizes plausible arguments and interpretations that decision-makers can utilize. Critical event interpretation should take advantage of Causal AIbased machine learning to produce practical knowledge for high-stakes decisions This needs causal knowledge produced by human-like intelligent agents, which will help interpret the events that may critically influence the future
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