Challenges of human—machine collaboration in risky decision-making

Abstract

The purpose of this paper is to delineate the research challenges of human—machine collaboration in risky decision-making. Technological advances in machine intelligence have enabled a growing number of applications in human—machine collaborative decision-making. Therefore, it is desirable to achieve superior performance by fully leveraging human and machine capabilities. In risky decision-making, a human decision-maker is vulnerable to cognitive biases when judging the possible outcomes of a risky event, whereas a machine decision-maker cannot handle new and dynamic contexts with incomplete information well. We first summarize features of risky decision-making and possible biases of human decision-makers therein. Then, we argue the necessity and urgency of advancing human—machine collaboration in risky decision-making. Afterward, we review the literature on human—machine collaboration in a general decision context, from the perspectives of human—machine organization, relationship, and collaboration. Lastly, we propose challenges of enhancing human—machine communication and teamwork in risky decision-making, followed by future research avenues.

Mapping the knowledge domain of soft computing applications for emergency evacuation studies: A scientometric analysis and critical review

“…The term "machine" here represents an intelligent system that makes decisions autonomously and independently (wholly or partly), and its autonomy is primarily achieved through simulation, artificial intelligence, and heuristic algorithms (Roth et al, 2019;Xiong et al, 2022;Li et al, 2022a). In order to make an evacuation decision, expert experience plays a critical role, especially in the preparation of evacuation plans, safety evacuation regulations, and rescue resource allocation, and often relies on expert knowledge and previous experience (Korbmacher and Tordeux, 2021).…”

Enhancing High-School Dropout Identification: A Collaborative Approach Integrating Human and Machine Insights

“…To address the imbalance issue, we utilized a hybrid resampling technique involving both the Synthetic Minority Oversampling Technique for Nominal and Continuous (SMOTE-NC) and Random Undersampling (RUS) [11,35].…”

Towards a Human-Centric Digital Twin for Human–Machine Collaboration: A Review on Enabling Technologies and Methods

“…HMC today faces many identical and social challenges addressed in the literature [3,5,18,19], such as transparency, explainability, technology acceptance and trust, safety, performance measures, training people, or decision-making risks while using AI [20]. Overcoming these challenges would result in improved human well-being and flexible manufacturing, where humans and machines develop their capabilities.…”