Risk Analysis of the Application of Artificial Intelligence in Public Management

2021 ◽  
pp. 587-595
Author(s):  
Min Kuang
Keyword(s):  
Artificial Intelligence ◽  
Risk Analysis ◽  
Public Management

Artificial intelligence treating the problem of uncertainty in quantitative risk analysis (QRA)

2019 ◽  
Vol 18 (1) ◽  
pp. 40-54
Author(s):  
Mohamed Seddik Hellas ◽  
Rachid Chaib ◽  
Ion Verzea
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Risk Analysis ◽  
Lung Damage ◽  
Whole Body ◽  
Individual Risk ◽  
Content Type ◽  
Quantitative Risk Analysis ◽  
Accident Scenario ◽  
Imperfect Knowledge

Purpose Nowadays, artificial intelligence computational methods, such as knowledge-based systems, neural networks, genetic algorithms and fuzzy logic, have been increasingly applied to several industrial research studies, the purpose of this paper is to study the contribution of fuzzy and possibilistic techniques to quantitative risk analysis (QRA) in the presence of imperfect knowledge about the occurrence and consequences of accidental phenomena. Design/methodology/approach To solve the problem of uncertainties related to the elements of the accident scenario such as the frequency and severity of the consequences, the authors used fuzzy logic. Using this type of analysis, it is possible to visualize the contours of the dead or fuzzy injury by fireball thermal effect (first- and second-degree burn, death) and lesions caused by vapor cloud explosion overpressure (lung damage, eardrum rupture, head impact, whole-body displacement). The frequency and severity of fuzzy results are calculated by extended multiplication using the alpha-cuts method. Findings This research project aims to reflect the real situation in the in Amenas industrial area (SONATRACH company), specifically the liquefied petroleum gas storage tank On-Spec 05-V-411A, to deal with this type of risk. Using this analysis allows us to estimate the fuzzy individual risk using the approach of fuzzy logic to treating this uncertainty in the parameter information of accident scenarios. This index individuel risk (IR) was evaluated against the criterion of acceptability and then used for decision-making in the field of industrial risk analysis and evaluation. Originality/value The originality of the work is to identify the weak points of the classical QRA to solve the problem of the uncertainties related to the elements of the accident scenario such as the frequency and severity of the consequences to visualize the fuzzy risk contours. On the one hand and the development of software to calculate the probability of death by the overpressure effect and classify the most sensitive organs on the other hand. Given the importance of this study, it can be generalized for similar sites in the region.

scholarly journals The application of artificial intelligence to pest risk analysis

2002 ◽  
Vol 55 ◽  
pp. 430-430
Author(s):  
L. Peacock ◽  
S.P. Worner ◽  
S. Samarasinghe
Keyword(s):  
Artificial Intelligence ◽  
Risk Analysis ◽  
Pest Risk Analysis ◽  
Pest Risk

Artificial Discretion as a Tool of Governance: A Framework for Understanding the Impact of Artificial Intelligence on Public Administration

2019 ◽  
Author(s):  
Matthew M Young ◽  
Justin B Bullock ◽  
Jesse D Lecy
Keyword(s):  
Artificial Intelligence ◽  
Public Administration ◽  
Public Management ◽  
Information Communication ◽  
Environmental Complexity ◽  
Task Specificity ◽  
Political Feasibility ◽  
Public Managers ◽  
New Information ◽  
The Impact

Abstract Public administration research has documented a shift in the locus of discretion away from street-level bureaucrats to “systems-level bureaucracies” as a result of new information communication technologies that automate bureaucratic processes, and thus shape access to resources and decisions around enforcement and punishment. Advances in artificial intelligence (AI) are accelerating these trends, potentially altering discretion in public management in exciting and in challenging ways. We introduce the concept of “artificial discretion” as a theoretical framework to help public managers consider the impact of AI as they face decisions about whether and how to implement it. We operationalize discretion as the execution of tasks that require nontrivial decisions. Using Salamon’s tools of governance framework, we compare artificial discretion to human discretion as task specificity and environmental complexity vary. We evaluate artificial discretion with the criteria of effectiveness, efficiency, equity, manageability, and political feasibility. Our analysis suggests three principal ways that artificial discretion can improve administrative discretion at the task level: (1) increasing scalability, (2) decreasing cost, and (3) improving quality. At the same time, artificial discretion raises serious concerns with respect to equity, manageability, and political feasibility.

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