Journal of Decision Systems

Augmented Intelligence for Smarter Organisations

In an era where the integration of technology and human intelligence defines the boundaries of innovation, this special issue is dedicated to this topic: "Augmented Intelligence for Smarter Organisations". This theme underscores the evolving symbiosis between human cognitive capabilities and artificial intelligence (AI), aiming to enhance organizational decision-making processes.

Augmented Intelligence and its implications

Augmented Intelligence represents a paradigm shift from traditional AI applications, focusing on the enhancement of human decision-making through the collaborative efforts of humans and AI systems. This concept transcends the notion of AI as a replacement for human intelligence, advocating for a harmonious integration that amplifies human capabilities without supplanting them. According to Harfouche, Quinio, & Bugiotti, (2023), organisations are increasingly adopting collaborative decision-making frameworks that leverage the strengths of both humans and AI, leading to more informed and effective outcomes.

This special issue aims to delve into the aggregated decision-making models, such as the one proposed by Shrestha et al. (2019), which serves as a cornerstone for understanding the mechanics of Augmented Intelligence. Such model elucidates the processes through which AI can assist in organisational decision-making by augmenting, rather than replacing, human knowledge and intuition.

Moreover, the recent development in Human-Centric Explainable Informed AI (Harfouche et al. 2023) involves a two-way interaction between user and AI. From one side, "Informed AI" refers to artificial intelligence systems that are developed and operate with a comprehensive awareness and integration of relevant data, context, and knowledge to make decisions or provide insights (Johnson, AlBizri, Harfouche A., Fosso-Wamba, 2021). Unlike conventional AI systems, which might work with limited or narrowly defined data sets, informed AI leverages a broader spectrum of information, including real-time data, historical data, contextual understanding, and even domain-specific knowledge, to enhance its decision-making capabilities. From another side, Explainable AI refers to AI systems designed to provide insights into how and why they arrived at a particular decision or output (Johnson, AlBizri, Harfouche, & Tutun, 2023). The goal is to make AI's decision-making process transparent, understandable, and interpretable for humans. This is in contrast to the "black box" nature of many AI models, where the decision-making process is opaque and difficult for humans to understand.

Challenges and Future Directions

Despite the progress in XAI, there are ongoing challenges, including the trade-off between model complexity and interpretability, ensuring accuracy in explanations, and aligning AI explanations with human ethical and moral values. Future directions may involve developing standardized frameworks for XAI (Johnson, M., AlBizri, A. Harfouche, A., & Tutun, S., 2021), improving methods for testing and validating explanations, and exploring interdisciplinary approaches that incorporate psychology, ethics, and social sciences to design truly human-centric AI systems.

In summary, Human-centric Explainable informed AI aims to bridge the gap between advanced AI technologies and the humans who use them, ensuring that AI's decision-making is accessible, understandable, and trustworthy. This not only enhances user experience and trust in AI systems but also ensures that AI is used responsibly and ethically in various domains.

A significant focus of this special issue will be on Responsible AI (Johnson, M., AlBizri, A., & Harfouche, A., 2021) and ethical AI (Tutun, S., Harfouche, A., AlBizri, A., Johnson, M., & Haiyue, H. 2023) and its role in augmenting human intelligence by mitigating AI biases (Harfouche, A., Quinio, B., & Bugiotti, F., 2023). The advent of Human-Centric AI emphasizes the importance of ethical considerations and the need for AI systems that complement human values and decision-making processes. This approach is critical in ensuring that the deployment of AI technologies contributes positively to societal advancement and the betterment of human lives.

Special issue objectives

• To explore the latest advancements in Augmented Intelligence and its applications in various sectors.
• To discuss the ethical, social, and technological implications of integrating AI with human decision-making.
• To foster interdisciplinary collaboration among researchers, practitioners, and policymakers in the field of AI and human augmentation.
• To showcase innovative solutions and case studies that highlight the practical applications of Augmented Intelligence in creating smarter organisations.

Call for Contributions

We invite researchers, industry experts, and practitioners to contribute their insights, research findings, and experiences related to Augmented Intelligence. By sharing diverse perspectives on the integration of AI in enhancing human decision-making, we aim to chart a path forward for the development of smarter, more responsive organisations.

 

References

Harfouche, A., Quinio, B., Saba, M., & Saba, P. (2023). « The Recursive Theory of Knowledge Augmentation: Integrating human intuition and knowledge in Artificial Intelligence to augment organizational knowledge », Information Systems Frontiers, 25, 55–70. https://doi.org/10.1007/s10796-022-10352-8

Shrestha, Y. R., Ben-Menahem, S. M., & Von Krogh, G. (2019). « Organizational decision-making structures in the age of artificial intelligence ». California Management Review, 61(4) 66-83.

Harfouche, A., Quinio, B., & Bugiotti, F. (2023). « Human-Centric AI to Mitigate AI Biases: The Advent of Augmented Intelligence », Journal of Global Information Management, 31(5), DOI: 10.4018/JGIM.331755

Johnson, M., AlBizri, A., Harfouche A., & Fosso-Wamba, S., (2022). « Integrating Human Domain Knowledge into Artificial Intelligence: Informed Artificial Intelligence ». International Journal of Information Management, Vol. 64. https://doi.org/10.1016/j.ijinfomgt.2022.102479

Johnson, M., AlBizri, A., Harfouche, A., & Tutun, S., (2023). « Digital Transformation to Mitigate Emergency Situations: Increasing Opioid Overdose Survival Rates through Explainable Artificial Intelligence », Industrial Management & Data Systems, 123(1), https://doi.org/10.1108/IMDS-04-2021-0248

Tutun, S., Harfouche, A., AlBizri, A., Johnson, M., & Haiyue, H. (2023). « A Responsible AI Framework to Mitigate the Ramifications of Organ Donation Crisis »., Information Systems Frontiers, 25 (6), 2301-2316. https://doi.org/10.1007/s10796-022-10340-y

Johnson, M., AlBizri, A., & Harfouche, A., (2021), « Responsible Artificial Intelligence in Healthcare: Predicting and Preventing Insurance Claim Denials for Economic and Social Wellbeing ». Information Systems Frontiers, 25 (6), https://doi.org/10.1007/s10796-021-10137-5

Johnson, M., AlBizri, A. Harfouche, A., & Tutun, S., (2021). « Digital Transformation to Mitigate Emergency Situations: Increasing Opioid Overdose Survival Rates through Explainable Artificial Intelligence », Industrial Management & Data Systems, 123 (1), 324-344. (2023) https://doi.org/10.1108/IMDS-04-2021-0248

 

Guest editors BIO

Antoine Harfouche, PhD is an Associate Professor of Information Systems (IS) and Artificial Intelligence (AI) at University Paris Nanterre France. He was awarded the AIS Sandra Slaughter Outstanding Service Award in 2020. Dr. Harfouche completed his M.Sc. and Ph.D. in Management Information Systems at Paris Dauphine University. His research primarily examines how IS and AI impact individuals, organizations, and countries. His publications appeared in peer reviewed journals (e.g., Information Systems Frontiers, International Journal of Information Management, Industrial Management & Data Systems, the Annals of Operation Research, Trends in Plant Science, Trends in Biotechnology, Information Technology and People, Journal of Global Information Management, Journal of Organizational and End User Computing, Informatics, …) and renowned conference proceedings (e.g., ICIS, AMCIS, ICTO,..). He is also a member of the editorial advisory board of the Journal of Enterprise Information Management. Throughout his career, Dr. Harfouche has obtained a considerable amount of funding—above 2 million dollars—from the French Research Council (ANR) and the European ERANSMUS +. In the ANR SCHOPPER project (ANR-DS0701/2016), he designed a new AI framework called Informed AI, in which human input is considered as an inextricable part of AI applications. In the ERASMUS + project VRAILEXIA, Dr. Harfouche has co-designed a strategic partnership project called “Partnership outside of the box: integrating artificial intelligence tools with Virtual reality to support higher education students with dyslexia.” This innovative project was given a score of 98 out of 100 by the ERASMUS + agency, thus achieved first place in 2020.

Peter Bou Saba, PhD, Associate Professor of Information Systems at EMLV Business School (Paris, La Défense) and director of the Master’s program in Management Information Systems. He conducts research on digital maturity and readiness, as well as on conflict prevention before and during IT implementation. Peter is author of several book chapters and articles related to user resistance & conflict prevention and digital transformation initiatives. He is also co-founder of Saba Business Circle (SBC), a European consulting firm specializing in innovation networks and management. Previously, Peter worked for several years in the field of innovation financing, and more recently, as a research director of In Extenso Innovation Croissance, a Deloitte France entity.

Mario Saba, PhD is a Professor of Information Systems (IS) at Business School Lausanne, IFM Business School Geneva, and the American University of applied sciences institute in Switzerland. He is fellow faculty at Washington State University, Carson College of Business. Dr. Saba is a researcher and expert in weak signals pertaining to informational intelligence. Respectively, he undertakes applied research that tackles IS technology uses, decision-making tools, artificial and augmented intelligence. Mario Saba is the founder of the Higher Hospitality Academy of Switzerland, an academy that undertakes applied research projects in IS, and converts hospitality values into competitive advantage. Respectively, he has developed consultancy and coaching programs for professionals across different industries.

Editorial Board

Abdullah Albizri, Montclair State University, USA
Adriana Schiopoiu Burlea, University of Craiova, Romania
Ananth Chiravuri, Al Ain University, UAE
Cecilia Rossignoli, University of Verona, Italy
Cinzia Dal Zotto, Neuchâtel University, Switzerland
Farhana Faruqe, University of Virginia, USA
Farid Nakhle, Temple University, Japan Campus (TUJ), Japan
Hajer Kefi, EMLV, France
Imed Ben Nasr, Excelia Group, France
Insaf Khelladi, EMLV, France
Jessy Nair, St. Joseph Institute of Management, India
Jihane Aayale, Groupe ISCAE, Morocco
Jonna Järveläinen, University of Jyväskylä, Finland
Leonel Matar, Saint Joseph University, Lebanon
Marco de Marco, Uninettuno University, Italy
Mousa Albashrawi, KFUPM, Saudi Arabia
Pascale Bueno Merino, EMLV, France
Samuel Fosso Wamba, TBS Education, France
Tetsuo Noda, Shimane University, Japan
Twinomurinzi Hossana, University of Johannesburg, South Africa
Wesley Palmer, York College, City University of New York, USA

Important Dates (Tentative schedule)

From June 1st, 2024 to Sep 1st, 2024	Deadline for submission of papers to the Special Issue
Oct 1st, 2024	Authors advised regarding paper acceptance for review
Dec1st, 2024	The first round of reviews was completed, and authors advised regarding review outcomes.
Feb 1st, 2025	Deadline for revised papers
April 1st, 2025	The second round of reviews was completed, and the authors advised regarding review outcomes.
June 1st, 2025	Deadline for revised papers
July 1st, 2025	Final editorial decision on papers accepted for the Special Issue
Aug 1st, 2025	Special Issue papers submitted to JDS for publication

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