Applying artificial intelligence technology to support decision-making in nursing: A case study in Taiwan

2015 ◽  
Vol 21 (2) ◽  
pp. 137-148 ◽  
Author(s):  
Pei-Hung Liao ◽  
Pei-Ti Hsu ◽  
William Chu ◽  
Woei-Chyn Chu
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Artificial Intelligence Technology ◽  
Support Decision Making

scholarly journals How Cognitive Biases Influence the Data Verification of Safety Indicators: A Case Study in Rail

Safety ◽  
2019 ◽  
Vol 5 (4) ◽  
pp. 69
Author(s):  
Burggraaf ◽  
Groeneweg ◽  
Sillem ◽  
van Gelder
Keyword(s):  
Decision Making ◽  
Cognitive Biases ◽  
Decision Makers ◽  
Human Cognition ◽  
Quality Data ◽  
Verification Process ◽  
Support Decision Making ◽  
Good Quality Data

The field of safety and incident prevention is becoming more and more data based. Data can help support decision making for a more productive and safer work environment, but only if the data can be, is and should be trusted. Especially with the advance of more data collection of varying quality, checking and judging the data is an increasingly complex task. Within such tasks, cognitive biases are likely to occur, causing analysists to overestimate the quality of the data and safety experts to base their decisions on data of insufficient quality. Cognitive biases describe generic error tendencies of persons, that arise because people tend to automatically rely on their fast information processing and decision making, rather than their slow, more effortful system. This article describes five biases that were identified in the verification of a safety indicator related to train driving. Suggestions are also given on how to formalize the verification process. If decision makers want correct conclusions, safety experts need good quality data. To make sure insufficient quality data is not used for decision making, a solid verification process needs to be put in place that matches the strengths and limits of human cognition.

Algorithmic decision-making? The user interface and its role for human involvement in decisions supported by artificial intelligence

Organization ◽  
2019 ◽  
Vol 26 (5) ◽  
pp. 655-672 ◽  
Author(s):  
Verena Bader ◽  
Stephan Kaiser
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
User Interface ◽  
User Involvement ◽  
Traditional View ◽  
Telecommunications Company

Artificial intelligence can provide organizations with prescriptive options for decision-making. Based on the notions of algorithmic decision-making and user involvement, we assess the role of artificial intelligence in workplace decisions. Using a case study on the implementation and use of cognitive software in a telecommunications company, we address how actors can become distanced from or remain involved in decision-making. Our results show that humans are increasingly detached from decision-making spatially as well as temporally and in terms of rational distancing and cognitive displacement. At the same time, they remain attached to decision-making because of accidental and infrastructural proximity, imposed engagement, and affective adhesion. When human and algorithmic intelligence become unbalanced in regard to humans’ attachment to decision-making, three performative effects result: deferred decisions, workarounds, and (data) manipulations. We conceptualize the user interface that presents decisions to humans as a mediator between human detachment and attachment and, thus, between algorithmic and humans’ decisions. These findings contrast the traditional view of automated media as diminishing user involvement and have useful implications for research on artificial intelligence and algorithmic decision-making in organizations.

scholarly journals A Predictive Analytics Model for E-commerce Sales Transactions to Support Decision Making: A Case Study

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Shereen Morsi
Keyword(s):  
Decision Making ◽  
Predictive Analytics ◽  
Strategic Decisions ◽  
Business Decision ◽  
Innovative Capabilities ◽  
Data Volume ◽  
Precautionary Measures ◽  
Support Decision Making

Given the significant growth in electronic commerce, firms are seeking technological innovations and innovative capabilities to deal concurrently with the data’ volume generated and gaining insights from it for better decisions. Although recent studies identify predictive analytics as becoming the keystone of all business decision making and a crucial aspect in firms by it is a possible means for driving strategic decisions. Significant inroads into the interrelationships between capabilities and the execution of a pathway to an analytical capability to many Egyptian e-commerce businesses have yet to be made. Therefore, this paper aims to shed light on the importance and the role of using predictive analytics models in the Egyptian e-commerce firms where these tools became dominant resources for gaining valuable knowledge for better decision making by precautionary measures from prediction rates and different applications that have been applied by global e-commerce firms. The aim of the paper was achieved by building a predictive analytics model for sales forecasting by tackling to one of the e-commerce company in Egypt, and the online transaction dataset has been analyzed. The result obtained from the model has been displayed, and some insights extracted from the prediction model have been explained.

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