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Abstract

Decision is obviously related to reasoning. One of the possible definitions of artificial intelligence (AI) refers to cognitive processes and especially to reasoning. Before making any decision, people also reason, it is therefore natural to explore the links between AI and decision making. This paper distinguishes between two aspects of decision making: diagnosis and look-ahead. It is shown that, on the one hand, AI has many relationships with diagnosis (expert systems, case-based reasoning, fuzzy set and rough set theories). On the other hand, AI has not paid enough attention to look-ahead reasoning, whose main components are uncertainty and preferences. These aspects of AI and decision making are reviewed in the paper.
... However, this is also a prediction that Prevedello et al [57] found to have gone unfulfilled from previous studies. Pomerol [58] discussed the issue of AI and human decision-making. He described AI as sharing several relationships with other types of quantitative analytical procedures in that each is useful in diagnosis. ...
... He described AI as sharing several relationships with other types of quantitative analytical procedures in that each is useful in diagnosis. He also noted that a critical limitation of AI was the lack of capacity for look-ahead reasoning, where uncertainty and preferences are crucial factors to consider [58]. ...
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The health care management and the medical practitioner literature lack a descriptive conceptual framework for understanding the dynamic and complex interactions between clinicians and artificial intelligence (AI) systems. As most of the existing literature has been investigating AI's performance and effectiveness from a statistical (analytical) standpoint, there is a lack of studies ensuring AI's ecological validity. In this study, we derived a framework that focuses explicitly on the interaction between AI and clinicians. The proposed framework builds upon well-established human factors models such as the technology acceptance model and expectancy theory. The framework can be used to perform quantitative and qualitative analyses (mixed methods) to capture how clinician-AI interactions may vary based on human factors such as expectancy, workload, trust, cognitive variables related to absorptive capacity and bounded rationality, and concerns for patient safety. If leveraged, the proposed framework can help to identify factors influencing clinicians' intention to use AI and, consequently, improve AI acceptance and address the lack of AI accountability while safeguarding the patients, clinicians, and AI technology. Overall, this paper discusses the concepts, propositions, and assumptions of the multidisciplinary decision-making literature, constituting a sociocognitive approach that extends the theories of distributed cognition and, thus, will account for the ecological validity of AI. (JMIR Hum Factors 2022;9(2):e35421)
... is method was used for many medical applications such as those in [7,8] and engineering applications such as those in [9,10], and others such as those in [2,[11][12][13][14][15]. ...
... In dissimilarity, a variable precision model can be used to improve it. When we compare our method to Pawlak's method [8] for determining decision-making accuracy, we get the same result. ...
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Not only mathematical statistics, differential equations, and mathematical models were used to analyze and reduce data, but a rough set model is also employed in medical, engineering, and other fields to analyze and reduce them. The goal of this paper is to introduce a minimal structure concept to produce new rough set models and show that it is suitable for analyzing most real-life problems, reduction of attributes, and decision making. We examine the effectiveness of the following method in the problem of electric power generators and decision making. We also offer a comparison of our method and Pawlak’s method. Finally, the variable precision model improves the accuracy of decision making.
... assistance in diagnosis and (2) assistance in doctor × patient interaction. e respondents' reports showed that the aid in the diagnosis occurs through: (1) complementation of the clinical diagnosis and the (2) level of confidence in the diagnosis offered by the system [13]. e question of AI replacing or complementing clinical diagnosis is still discussed in the literature [12]. ...
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Technologies are increasingly independent and play important roles in society. Artificial intelligence (AI) is a branch of science that can improve various environments and processes. The health sector stands out among these contexts, especially ophthalmology and dentistry. Studies evaluating the impact of using these technologies in these contexts are still developing. There are still few studies that assess how AI can impact the decision-making process of health professionals and how it can improve the quality of care provided to these professionals. In this sense, this study aims to evaluate the perception of the impact of AI on the decision-making process of health professionals and the quality of patient care from the perspective of ophthalmologists and dentists. The methodological strategy used was the application of an online questionnaire with eighteen professionals in these areas. Based on the respondents’ opinions, we sought to assess how these decision-making processes are affected by the use of technologies and how they impact the quality of patient care. As a result, it was observed that AI has become essential and a facilitator of the diagnostic processes. However, it presents some challenges related to cost, accessibility, AI x professional responsibility, and incentive of agreements.
... It is achieved by scenario building and what if analysis. The candidates for look ahead machines can be simulations or decision support systems [9]. ...
... Decision-making made by artificial intelligence is related to reasoning, so it is inevitable to understand the relationship between artificial intelligence and decision-making. This paper distinguishes two aspects of decision-making [2]. On the one hand, artificial intelligence has many relationships with diagnosis. ...
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With the development of the times, the progress of science and technology, and the growing needs of people, more and more urban community planning chooses artificial intelligence to replace the traditional urban community planning, at the same time, it also brings new influences and changes to children’s play environment. Firstly, GA, BP, and GA-BP algorithms are explained, and IGA-BP algorithm is proposed. Secondly, the planning and allocation schemes of different communities in children’s environment are planned, and genetic algorithm and BP neural network are used to plan and apply children’s play environment in urban communities. Finally, the experiment compares and predicts the optimal path of the above three algorithms, and the results show that IGA-BP algorithm can wait for the optimal path. Then, GA-BP and IGA-BP algorithms were used to compare the heat of children’s community and other related indicators. IGA-BP algorithm has obvious advantages in heat prediction, absolute error, and relative error.
... Humans make decisions by reasoning about their complex environments. Behavioral modeling aims to improve the decision processes by investigating the patterns of reasoning and making sense of those patterns [57]. Researchers in various felds have dedicated years to understanding human decision-making. ...
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This article aims to analyse whether the social control over the administration will play any important role when the administration is going to be performed through the Artificial Intelligence algorithms. It seems that currently this method of controlling the administration is important to ensure that the administration is still integrated into a democratic state ruled by law. However, applying the Artificial Intelligence in the administration process may lead to the situation where the transparency of administrative process is significantly reduced. This may be due to the nature of algorithms. The humans are not able to “decode” or recreate the way the algorithm investigates and solves the given problem. Therefore there is a risk that democratic state may turn into the “technological anocracy” or even into the “technological authoritarianism”. In addition, reckless use of Artificial Intelligence in the administrative process may reverse the values of administrative law itself understood as the law which protects an individual against the abuse of administrative authority. The paper contains also the suggestions in changing the way of exercise the social control over the administration to ensure the democratic standards in the administrative process.
Conference Paper
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Chapter
In this chapter the metasystem paradigm elaborated in chapters 14 and 15 is applied to decision making in real-worlds situations
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