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Human–AI Teaming: Synergy, Decision-Making and Interdependency

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A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Engineering".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 79012

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Special Issue Editors

Dr. Shixuan Fu

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Guest Editor

School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
Interests: distributed collaboration; collective wisdom; crowdsourcing and sharing economy; human-AI teaming

Dr. Bo Yang

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Guest Editor

School of Information, Renmin University of China, Beijing 100872, China
Interests: IT governance; team collaboration; IT outsourcing

Dr. Alex Zarifis

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Guest Editor

Dauphine Researchers in Management (DRM), PSL University, Pl. du Maréchal de Lattre de Tassigny, 75016 Paris, France
Interests: e-business; e-government; information system; practical applications of technology in business
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent progress in artificial intelligence (AI) has gradually made human–AI teaming possible. AI techniques assist or automate business operations and production practices. For example, employees and conversational agents closely cooperate to serve customers (Huang and Rust, 2018). Advanced algorithms have also been applied to predict future passenger traffic, assisting human decision makers. However, several issues arise from human–AI interactions (Zarifis et al., 2021). Individuals are sometimes exclusionary of AI teammates, especially when they perceive them as job competition (Seeber et al., 2020). Additionally, many AI techniques are still highly unadaptable (Madni and Madni, 2018), frustrating and disengaging their human collaborators, and individuals may feel emotionally uncomfortable when conflicts arise in human–AI teaming. Therefore, it is pivotal that human–AI interactions are coordinated to achieve a synergy between the involved collaborators. AI teammates should be reasonably designed and managed for collective intelligence and interdependent decision making.

Addressing the engineering side of AI and current behavioral studies on individuals and groups (Rahwan et al.,2019), this Special Issue encourages submissions on the following topics:

AI teammate design in a collaborative environment;
Trust in human–AI collaboration;
Prototyping for effective human–AI teaming;
Group dynamics in human–AI decision making;
Conflict management in human–AI teaming;
Knowledge management in human–AI teaming;
A systematic review on human–AI synergy;
Software-driven automation of human–AI interaction;
Behavior modeling of human–AI interdependency;
Security and privacy issues in human–AI synergy

References:

Huang, M. H., & Rust, R. T. Artifificial intelligence in service, Journal of Service Research, 2018, 21(2): 155–172.

Madni, A. M., & Madni, C. C.. Architectural framework for exploring adaptive human-machine teaming options in simulated dynamic environments. Systems, 2018, 6(4), 44.

Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J. F., Breazeal, C., ... & Jennings, N. R, Machine behaviour, Nature, 2019, 568(7753): 477-486.

Seeber, I., Bittner, E., Briggs, R. O., de Vreede, T., de Vreede, G. J., Elkins, A., ... & Schwabe, G. Machines as teammates: A research agenda on AI in team collaboration, Information & management, 2020, 57(2): 1-22.

Zarifis A., Kawalek P. & Azadegan A. Evaluating if Trust and Personal Information Privacy Concerns are Barriers to Using Health Insurance that Explicitly Utilizes AI, Journal of Internet Commerce, 2021, 20: 66-83.

Dr. Shixuan Fu
Dr. Bo Yang
Dr. Alex Zarifis
Guest Editors

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Keywords

artificial intelligence
human–AI teaming
team management
trust
AI teammates

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Published Papers (12 papers)

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Research

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20 pages, 2542 KiB

Open AccessArticle

Research on Users’ Exercise Behaviors of Online Exercise Community Based on Social Capital Theory

by Jing Fan, Xingchen Guo, Xuan Liu and Xinyi Xue

Systems 2023, 11(8), 411; https://doi.org/10.3390/systems11080411 - 9 Aug 2023

Viewed by 1461

Abstract

Online exercise communities play an important role in their users’ self-health management. The willingness of users to interact and create user-generated content in online communities reflects the vitality of the online exercise community and the positive impact it has on offline users’ health performance. Therefore, based on social capital theory, we study the relationship between three types of social capital and users’ offline exercise behaviors and add off-topics in the community in the model. We select the KEEP health community user group as the research setting and conduct the regression analysis. The results show that owned centrality and reciprocity have a significant positive relationship with users’ exercise behaviors; accessed centrality and trust have a significant negative relationship with users’ exercise behaviors; and common topics and off-topics show a partly significant correlation. As a moderating variable, off-topics have a negative moderating effect on owned centrality and betweenness centrality, but a positive moderating effect on reciprocity and trust among group members. The results enrich and expand social capital theory, deepen the research on users’ exercise behaviors in the online exercise community, and provide a good reference for online exercise community management. Full article

(This article belongs to the Special Issue Human–AI Teaming: Synergy, Decision-Making and Interdependency)

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26 pages, 4121 KiB

Open AccessArticle

Students’ Classroom Behavior Detection System Incorporating Deformable DETR with Swin Transformer and Light-Weight Feature Pyramid Network

by Zhifeng Wang, Jialong Yao, Chunyan Zeng, Longlong Li and Cheng Tan

Systems 2023, 11(7), 372; https://doi.org/10.3390/systems11070372 - 20 Jul 2023

Cited by 9 | Viewed by 2234

Abstract

Artificial intelligence (AI) and computer vision technologies have gained significant prominence in the field of education. These technologies enable the detection and analysis of students’ classroom behaviors, providing valuable insights for assessing individual concentration levels. However, the accuracy of target detection methods based on Convolutional Neural Networks (CNNs) can be compromised in classrooms with multiple targets and varying scales, as convolutional operations may result in the loss of location information. In contrast, transformers, which leverage attention mechanisms, have the capability to learn global features and mitigate the information loss caused by convolutional operations. In this paper, we propose a students’ classroom behavior detection system that combines deformable DETR with a Swin Transformer and light-weight Feature Pyramid Network (FPN). By employing a feature pyramid structure, the system can effectively process multi-scale feature maps extracted by the Swin Transformer, thereby improving the detection accuracy for targets of different sizes and scales. Moreover, the integration of the CARAFE lightweight operator into the FPN structure enhances the network’s detection accuracy. To validate the effectiveness of our approach, extensive experiments are conducted on a real dataset of students’ classroom behavior. The experimental results demonstrate a significant 6.1% improvement in detection accuracy compared to state-of-the-art methods. These findings highlight the superiority of our proposed network in accurately detecting and analyzing students’ classroom behaviors. Overall, this research contributes to the field of education by addressing the limitations of CNN-based target detection methods and leveraging the capabilities of transformers to improve accuracy. The proposed system showcases the benefits of integrating deformable DETR, Swin Transformer, and the lightweight FPN in the context of students’ classroom behavior detection. The experimental results provide compelling evidence of the system’s effectiveness and its potential to enhance classroom monitoring and assessment practices. Full article

(This article belongs to the Special Issue Human–AI Teaming: Synergy, Decision-Making and Interdependency)

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14 pages, 2374 KiB

Open AccessArticle

Designing Effective Instructional Feedback Using a Diagnostic and Visualization System: Evidence from a High School Biology Class

by Lin Ma, Xuedi Zhang, Zhifeng Wang and Heng Luo

Systems 2023, 11(7), 364; https://doi.org/10.3390/systems11070364 - 17 Jul 2023

Cited by 2 | Viewed by 1852

Abstract

Although instructional feedback plays an essential role in regulating learning and improving performance, few studies have systematically investigated the needs of teachers and students for instructional feedback systems or developed designs and experiments, especially at the high school level. To address this research need, the present study investigated the needs of selected students and teachers in a high school in Hubei Province, China, and designed and developed a diagnostic visual feedback system for an experimental study with 125 students from a 10th-grade biology class in the same high school. The results showed that this diagnostic visual feedback report improved student performance (ES = 0.37) and that functions such as misconception location, knowledge diagnosis, and knowledge alert were well received by students. These findings have multiple implications for facilitating the design and development of diagnostic visual feedback systems. Full article

(This article belongs to the Special Issue Human–AI Teaming: Synergy, Decision-Making and Interdependency)

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37 pages, 18116 KiB

Open AccessArticle

Harnessing the Power of ChatGPT for Automating Systematic Review Process: Methodology, Case Study, Limitations, and Future Directions

by Ahmad Alshami, Moustafa Elsayed, Eslam Ali, Abdelrahman E. E. Eltoukhy and Tarek Zayed

Systems 2023, 11(7), 351; https://doi.org/10.3390/systems11070351 - 9 Jul 2023

Cited by 42 | Viewed by 16595

Abstract

Systematic reviews (SR) are crucial in synthesizing and analyzing existing scientific literature to inform evidence-based decision-making. However, traditional SR methods often have limitations, including a lack of automation and decision support, resulting in time-consuming and error-prone reviews. To address these limitations and drive the field forward, we harness the power of the revolutionary language model, ChatGPT, which has demonstrated remarkable capabilities in various scientific writing tasks. By utilizing ChatGPT’s natural language processing abilities, our objective is to automate and streamline the steps involved in traditional SR, explicitly focusing on literature search, screening, data extraction, and content analysis. Therefore, our methodology comprises four modules: (1) Preparation of Boolean research terms and article collection, (2) Abstract screening and articles categorization, (3) Full-text filtering and information extraction, and (4) Content analysis to identify trends, challenges, gaps, and proposed solutions. Throughout each step, our focus has been on providing quantitative analyses to strengthen the robustness of the review process. To illustrate the practical application of our method, we have chosen the topic of IoT applications in water and wastewater management and quality monitoring due to its critical importance and the dearth of comprehensive reviews in this field. The findings demonstrate the potential of ChatGPT in bridging the gap between traditional SR methods and AI language models, resulting in enhanced efficiency and reliability of SR processes. Notably, ChatGPT exhibits exceptional performance in filtering and categorizing relevant articles, leading to significant time and effort savings. Our quantitative assessment reveals the following: (1) the overall accuracy of ChatGPT for article discarding and classification is 88%, and (2) the F-1 scores of ChatGPT for article discarding and classification are 91% and 88%, respectively, compared to expert assessments. However, we identify limitations in its suitability for article extraction. Overall, this research contributes valuable insights to the field of SR, empowering researchers to conduct more comprehensive and reliable reviews while advancing knowledge and decision-making across various domains. Full article

(This article belongs to the Special Issue Human–AI Teaming: Synergy, Decision-Making and Interdependency)

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15 pages, 677 KiB

Open AccessArticle

Exploring Trust in Human–AI Collaboration in the Context of Multiplayer Online Games

by Keke Hou, Tingting Hou and Lili Cai

Systems 2023, 11(5), 217; https://doi.org/10.3390/systems11050217 - 24 Apr 2023

Cited by 4 | Viewed by 3848

Abstract

Human–AI collaboration has attracted interest from both scholars and practitioners. However, the relationships in human–AI teamwork have not been fully investigated. This study aims to research the influencing factors of trust in AI teammates and the intention to cooperate with AI teammates. We conducted an empirical study by developing a research model of human–AI collaboration. The model presents the influencing mechanisms of interactive characteristics (i.e., perceived anthropomorphism, perceived rapport, and perceived enjoyment), environmental characteristics (i.e., peer influence and facilitating conditions), and personal characteristics (i.e., self-efficacy) on trust in teammates and cooperative intention. A total of 423 valid surveys were collected to test the research model and hypothesized relationships. The results show that perceived rapport, perceived enjoyment, peer influence, facilitating conditions, and self-efficacy positively affect trust in AI teammates. Moreover, self-efficacy and trust positively relate to the intention to cooperate with AI teammates. This study contributes to the teamwork and human–AI collaboration literature by investigating different antecedents of the trust relationship and cooperative intention. Full article

(This article belongs to the Special Issue Human–AI Teaming: Synergy, Decision-Making and Interdependency)

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21 pages, 2362 KiB

Open AccessArticle

Leveraging Ethereum Platform for Development of Efficient Tractability System in Pharmaceutical Supply Chain

by Muntaha Aslam, Sohail Jabbar, Qaisar Abbas, Mubarak Albathan, Ayyaz Hussain and Umar Raza

Systems 2023, 11(4), 202; https://doi.org/10.3390/systems11040202 - 17 Apr 2023

Cited by 3 | Viewed by 4347

Abstract

Consumer knowledge of the goods produced or processed by the numerous suppliers and processors is still relatively low due to the growing complexity of the structure of pharmaceutical supply chains. Information asymmetry in the pharmaceutical sector has an effect on welfare, sustainability, and health. (1) Background: In this respect, we wanted to develop a productive structure for a pharmaceutical supply chain that satisfies the consumer information needs and fosters consumer confidence in the pharmacy goods they buy. By using blockchain technology, the main goals were to develop and implement a pharmaceutical supply chain. (2) Objectives: The main objectives of this work were to leverage an Ethereum platform for the development of a tractability system in a pharmaceutical supply chain environment and to analyze the efficiency of MSMAChain with respect to the cost and execution of transactions based on our designed smart contracts. (3) Results: This research looked into a variety of issues related to the value, viability, and effects of blockchain technology for use in supply chain applications. The methods and creations in this environment were monitored and researched. It is vital to identify a number of crucial subjects including future research areas, in order to achieve the widespread acceptance of the supply chain traceability provided by blockchain technology. (4) Conclusions: MSMAChain, an Ethereum blockchain-based approach, leverages smart contracts and decentralized off-chain storage for efficient product traceability in terms of the cost and execution of transaction for a health care supply chain. Full article

(This article belongs to the Special Issue Human–AI Teaming: Synergy, Decision-Making and Interdependency)

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14 pages, 2790 KiB

Open AccessArticle

A Multivariate Deep Learning Model with Coupled Human Intervention Factors for COVID-19 Forecasting

by Zongxi Qu, Beidou Zhang and Hongpeng Wang

Systems 2023, 11(4), 201; https://doi.org/10.3390/systems11040201 - 17 Apr 2023

Viewed by 1752

Abstract

Artificial intelligence (AI) technology plays a crucial role in infectious disease outbreak prediction and control. Many human interventions can influence the spread of epidemics, including government responses, quarantine, and economic support. However, most previous AI-based models have failed to consider human interventions when predicting the trend of infectious diseases. This study selected four human intervention factors that may affect COVID-19 transmission, examined their relationship to epidemic cases, and developed a multivariate long short-term memory network model (M-LSTM) incorporating human intervention factors. Firstly, we analyzed the correlations and lagged effects between four human factors and epidemic cases in three representative countries, and found that these four factors typically delayed the epidemic case data by approximately 15 days. On this basis, a multivariate epidemic prediction model (M-LSTM) was developed. The model prediction results show that coupling human intervention factors generally improves model performance, but adding certain intervention factors also results in lower performance. Overall, a multivariate deep learning model with coupled variable correlation and lag outperformed other comparative models, and thus validated its effectiveness in predicting infectious diseases. Full article

(This article belongs to the Special Issue Human–AI Teaming: Synergy, Decision-Making and Interdependency)

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15 pages, 1508 KiB

Open AccessArticle

Product Engineering Assessment of Subsea Intervention Equipment Using SWARA-MOORA-3NAG Method

by Pedro Gall Fernandes, Osvaldo Luiz Gonçalves Quelhas, Carlos Francisco Simões Gomes, Enderson Luiz Pereira Júnior, Ricardo Luiz Fernandes Bella, Claudio de Souza Rocha Junior, Ruan Carlos Alves Pereira, Marcio Pereira Basilio and Marcos dos Santos

Systems 2023, 11(3), 125; https://doi.org/10.3390/systems11030125 - 25 Feb 2023

Cited by 6 | Viewed by 2362

Abstract

Oilfields must increase their production due to the current price of oil barrels. The sale of these oilfields by big companies enabled new companies to enter the exploration and production segment of brownfields to increase oil and gas production through subsea intervention projects. However, these projects require specific product development that involves technical requirements that the engineering department must analyze. This research aims to apply the SWARA-MOORA-3NAG multicriteria decision analysis (MCDA) method in analyzing the technical proposals of subsea intervention equipment for ordering suppliers according to the engineering requirements defined at the initial stage of the projects of an oil and gas company. The research methodology was divided into five stages: (1) identification of the problem through observation of the current process and interviews with engineers; (2) data collection through bibliographic research in the Scopus database; (3) problem modeling; (4) proposition of the solution with the application of the SWARA-MOORA-3NAG method; and (5) analysis of the results found. The application of the SWARA-MOORA-3NAG method brought a new ordering of suppliers to the analyzed case, enabling comparison between the method previously used by the engineering department and the method proposed by this research, emphasizing that the MCDA methods can be inserted into the analysis processes of technical proposals in the engineering department of the company analyzed. Full article

(This article belongs to the Special Issue Human–AI Teaming: Synergy, Decision-Making and Interdependency)

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18 pages, 1309 KiB

Open AccessArticle

Artificial Intelligence and Ten Societal Megatrends: An Exploratory Study Using GPT-3

by Daniela Haluza and David Jungwirth

Systems 2023, 11(3), 120; https://doi.org/10.3390/systems11030120 - 24 Feb 2023

Cited by 73 | Viewed by 15542

Abstract

This paper examines the potential of artificial intelligence (AI) to address societal megatrends, with a specific focus on OpenAI’s Generative Pre-Trained Transformer 3 (GPT-3). To do this, we conducted an analysis using GPT-3 in order to explore the benefits of AI for digitalization, urbanization, globalization, climate change, automation and mobility, global health issues, and the aging population. We also looked at emerging markets as well as sustainability in this study. Interaction with GPT-3 was conducted solely through prompt questions, and generated responses were analyzed. Our results indicate that AI can significantly improve our understanding of these megatrends by providing insights into how they develop over time and which solutions could be implemented. Further research is needed to determine how effective AI will be in addressing them successfully, but initial findings are encouraging. Our discussion focuses on the implications of our findings for society going forward and suggests that further investigation should be conducted into how best to utilize new technologies such as GPT-3 when tackling these challenges. Lastly, we conclude that, while there is still much work left to do before any tangible effects can be seen from utilizing AI tools such as GPT-3 on societal megatrends, early indications suggest it may have a positive impact if used correctly. Full article

(This article belongs to the Special Issue Human–AI Teaming: Synergy, Decision-Making and Interdependency)

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26 pages, 1126 KiB

Open AccessArticle

Does Artificial Intelligence Promote or Inhibit On-the-Job Learning? Human Reactions to AI at Work

by Chao Li, Yuhan Zhang, Xiaoru Niu, Feier Chen and Hongyan Zhou

Systems 2023, 11(3), 114; https://doi.org/10.3390/systems11030114 - 22 Feb 2023

Cited by 15 | Viewed by 10109

Abstract

This paper examines how AI at work impacts on-the-job learning, shedding light on workers’ reactions to the groundbreaking AI technology. Based on theoretical analysis, six hypotheses are proposed regarding three aspects of AI’s influence on on-the-job learning. Empirical results demonstrate that AI significantly inhibits people’s on-the-job learning and this conclusion holds true in a series of robustness and endogeneity checks. The impact mechanism is that AI makes workers more pessimistic about the future, leading to burnout and less motivation for on-the-job learning. In addition, AI’s replacement, mismatch, and deskilling effects decrease people’s income while extending working hours, reducing their available financial resources and disposable time for further learning. Moreover, it has been found that AI’s impact on on-the-job learning is more prominent for older, female and less-educated employees, as well as those without labor contracts and with less job autonomy and work experience. In regions with more intense human–AI competition, more labor-management conflicts, and poorer labor protection, the inhibitory effect of AI on further learning is more pronounced. In the context of the fourth technological revolution driving forward the intelligent transformation, findings of this paper have important implications for enterprises to better understand employee behaviors and to promote them to acquire new skills to achieve better human–AI teaming. Full article

(This article belongs to the Special Issue Human–AI Teaming: Synergy, Decision-Making and Interdependency)

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14 pages, 2657 KiB

Open AccessArticle

MLA-LSTM: A Local and Global Location Attention LSTM Learning Model for Scoring Figure Skating

by Chaoyu Han, Fangyao Shen, Lina Chen, Xiaoyi Lian, Hongjie Gou and Hong Gao

Systems 2023, 11(1), 21; https://doi.org/10.3390/systems11010021 - 2 Jan 2023

Cited by 4 | Viewed by 2566

Abstract

Video-based scoring using neural networks is a very important means for evaluating many sports, especially figure skating. Although many methods for evaluating action quality have been proposed, there is no uniform conclusion on the best feature extractor and clip length for the existing methods. Furthermore, during the feature aggregation stage, these methods cannot accurately locate the target information. To address these tasks, firstly, we systematically compare the effects of the figure skating model with three different feature extractors (C3D, I3D, R3D) and four different segment lengths (5, 8, 16, 32). Secondly, we propose a Multi-Scale Location Attention Module (MS-LAM) to capture the location information of athletes in different video frames. Finally, we present a novel Multi-scale Location Attentive Long Short-Term Memory (MLA-LSTM), which can efficiently learn local and global sequence information in each video. In addition, our proposed model has been validated on the Fis-V and MIT-Skate datasets. The experimental results show that I3D and 32 frames per second are the best feature extractor and clip length for video scoring tasks. In addition, our model outperforms the current state-of-the-art method hybrid dynAmic-statiC conText-aware attentION NETwork (ACTION-NET), especially on MIT-Skate (by 0.069 on Spearman’s rank correlation). In addition, it achieves average improvements of 0.059 on Fis-V compared with Multi-scale convolutional skip Self-attentive LSTM Module (MS-LSTM). It demonstrates the effectiveness of our models in learning to score figure skating videos. Full article

(This article belongs to the Special Issue Human–AI Teaming: Synergy, Decision-Making and Interdependency)

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Review

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20 pages, 1140 KiB

Open AccessReview

A Literature Review of Human–AI Synergy in Decision Making: From the Perspective of Affordance Actualization Theory

by Ying Bao, Wankun Gong and Kaiwen Yang

Systems 2023, 11(9), 442; https://doi.org/10.3390/systems11090442 - 25 Aug 2023

Cited by 7 | Viewed by 11879

Abstract

The emergence of artificial-intelligence (AI)-powered information technology, such as deep learning and natural language processing, enables human to shift their behaving or working diagram from human-only to human–AI synergy, especially in the decision-making process. Since AI is multidisciplinary by nature and our understanding of human–AI synergy in decision-making is fragmented, we conducted a literature review to systematically characterize the phenomenon. Adopting the affordance actualization theory, we developed a framework to organize and understand the relationship between AI affordances, the human–AI synergy process, and the outcomes of human–AI synergy. Three themes emerged from the review: the identification of AI affordances in decision-making, human–AI synergy patterns regarding different decision tasks, and outcomes of human–AI synergy in decision-making. For each theme, we provided evidence on the existing research gaps and proposed future research directions. Our findings provide a holistic framework for understanding human–AI synergy phenomenon in decision-making. This work also offers theoretical contributions and research directions for researchers studying human–AI synergy in decision-making. Full article

(This article belongs to the Special Issue Human–AI Teaming: Synergy, Decision-Making and Interdependency)

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