Guest Editorial: Computational Intelligence and Applications

Sugumaran, Vijayan; Geetha, T. V.; Manjula, D.; Gopal, Hema

doi:10.1007/s10796-017-9794-7

Cited by 8 publications

(7 citation statements)

References 20 publications

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“…Theoretically and academically, this approach gives a revitalization of the importance of tacit knowledge stocks as the alpha and omega of organizational learning (Nonaka, 1994) and shows the relevance of this insight in the context of IA systems. This is in line with previous understanding of the difference between artificial or computational intelligence (Sugumaran et al, 2017) and intelligence amplification as a way of increasing human thinking power (single loop or double loop) with artificial intelligence as part of it (Berrar et al, 2013). However, more knowledge is needed of the diversity of organizational contexts and how these interact on the IA adoption process design and realization.…”

Section: Discussionsupporting

confidence: 85%

“…It primarily includes fuzzy logic systems, neural networks and evolutionary computation" (Sugumaran et al, 2017, p. 969). Many interesting technical developments in AI have been reported, some of the special issue of Information Systems Frontiers edited by Sugumaran et al (2017) but the social perspective of organizational contexts and user-AI interactions that make these technological developments have value in practice have been as yet under-researched (Harper, 2019). Therefore, this article takes a social perspective on AI adoption, with a focus on developing knowledge to let human-AI interactions be effective, which we call an organizational learning perspective (Crossan et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

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Organizational Learning for Intelligence Amplification Adoption: Lessons from a Clinical Decision Support System Adoption Project

Wijnhoven

2021

Inf Syst Front

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Intelligence amplification exploits the opportunities of artificial intelligence, which includes data analytic techniques and codified knowledge for increasing the intelligence of human decision makers. Intelligence amplification does not replace human decision makers but may help especially professionals in making complex decisions by well-designed human-AI system learning interactions (i.e., triple loop learning). To understand the adoption challenges of intelligence amplification systems, we analyse the adoption of clinical decision support systems (CDSS) as an organizational learning process by the case of a CDSS implementation for deciding on administering antibiotics to prematurely born babies. We identify user-oriented single and double loop learning processes, triple loop learning, and institutional deutero learning processes as organizational learning processes that must be realized for effective intelligence amplification adoption. We summarize these insights in a system dynamic model—containing knowledge stocks and their transformation processes—by which we analytically structure insights from the diverse studies of CDSS and intelligence amplification adoption and by which intelligence amplification projects are given an analytic theory for their design and management. From our case study, we find multiple challenges of deutero learning that influence the effectiveness of IA implementation learning as transforming tacit knowledge into explicit knowledge and explicit knowledge back to tacit knowledge. In a discussion of implications, we generate further research directions and discuss the generalization of our case findings to different organizations.

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Section: Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Organizational Learning for Intelligence Amplification Adoption: Lessons from a Clinical Decision Support System Adoption Project

Wijnhoven

2021

Inf Syst Front

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“…AI has shown immense potential to transform our society and lives (He et al, 2014;Lukić et al, 2017;Sugumaran et al, 2017). AI has gained popularity in the healthcare domain for its ability to replicate human cognition in the complex medical field, diagnose problems and identify trends that can be immensely useful in the treatment of critical diseases like cancer (Davenport & Kalakota, 2019;Mukherjee, 2020).…”

Section: Discussionmentioning

confidence: 99%

Role of Risks in the Development of Responsible Artificial Intelligence in the Digital Healthcare Domain

2021

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The use of artificial intelligence (AI) in the healthcare field is gaining popularity. However, it also raises some concerns related to privacy and ethical aspects that require the development of a responsible AI framework. The principle of responsible AI states that artificial intelligence-based systems should be considered a part of composite societal and technological systems. This study attempts to establish whether AI risks in digital healthcare are positively associated with responsible AI. The moderating effect of perceived trust and perceived privacy risks is also examined. The theoretical model was based on perceived risk theory. Perceived risk theory is important in the context of this study, as risks related to uneasiness and uncertainty can be expected in the development of responsible AI due to the volatile nature of intelligent applications. Our research provides some interesting findings which are presented in the discussion section.

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“…At present, one of the mostly used methods in the mutual conversion between quantitative data and qualitative concepts is cloud models, which can better solve the problem of randomness and fuzzy recognition [14]. Sugumaran et al [15] used a clustering algorithm to evaluate risk [16], which had the advantages of fast processing speed and high intelligence. Other methods such as fuzzy set theory [17][18][19][20][21], comprehensive safety assessment (CSA) methods [22], accident tree analysis methods [23][24][25], neural networks [26,27], and Gray theory [28][29][30] are also applied to the field of water traffic safety.…”

Section: Introductionmentioning

confidence: 99%

Risk Causal Analysis of Traffic-Intensive Waters Based on Infectious Disease Dynamics

Chen

Liu

Wan

2019

JMSE

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Accidents occur frequently in traffic-intensive waters, which restrict the safe and rapid development of the shipping industry. Due to the suddenness, randomness, and uncertainty of accidents in traffic-intensive waters, the probability of the risk factors causing traffic accidents is usually high. Thus, properly analyzing those key risk factors is of great significance to improve the safety of shipping. Based on the analysis of influencing factors of ship navigational risks in traffic-intensive waters, this paper proposes a cloud model to excavate the factors affecting navigational risk, which could accurately screen out the key risk factors. Furthermore, the risk causal model of ship navigation in traffic-intensive waters is constructed by using the infectious disease dynamics method in order to model the key risk causal transmission process. Moreover, an empirical study of the Yangtze River estuary is conducted to illustrate the feasibility of the proposed models. The research results show that the cloud model is useful in screening the key risk factors, and the constructed causal model of ship navigational risks in traffic-intensive waters is able to provide accurate analysis of the transmission process of key risk factors, which can be used to reduce the navigational risk of ships in traffic-intensive waters. This research provides both theoretical basis and practical reference for regulators in the risk management and control of ships in traffic-intensive waters.

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Guest Editorial: Computational Intelligence and Applications

Cited by 8 publications

References 20 publications

Organizational Learning for Intelligence Amplification Adoption: Lessons from a Clinical Decision Support System Adoption Project

Organizational Learning for Intelligence Amplification Adoption: Lessons from a Clinical Decision Support System Adoption Project

Role of Risks in the Development of Responsible Artificial Intelligence in the Digital Healthcare Domain

Risk Causal Analysis of Traffic-Intensive Waters Based on Infectious Disease Dynamics

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