Digital Education, Work and Artificial Intelligence: Health and Law

The use and possibilities of artificial intelligence (AI) have been assuming great importance in recent years. This fact led to a greater attention on the topic in various fields, especially in health and law, both in its daily application potential and in learning methods. The aim of this article was to present a brief perspective of the challenges and effects of the AI use in teaching and application on health and law domains. Therefore, to better define the theme it was performed a qualitative methodology of bibliographic review. The applications of artificial intelligence have a great potential in clinical and legal use, facilitating the tasks of those involved by helping to reduce workflow, to avoid errors and in decision-making. However, despite these benefits and new opportunities, there are still obstacles regarding regulation and ethical concerns, as well as some reluctance from professionals in their adoption and formal application. In addition, there also the need to proper implement these technologies in learning to keep up the change and the new challenges currently posed, so there is a path that still needs to be followed.

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Artificial Intelligence for Decision Makers

Journal of Emerging Technologies in Accounting ◽

10.2308/jeta-19-04-30-21 ◽

2019 ◽

pp. 0000-0000

Author(s):

Viktor Elliot ◽

Mari Paananen ◽

Miroslaw Staron

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Making ◽

Machine Learning Algorithms ◽

Decision Makers ◽

Accounting Data ◽

Ethical Concerns ◽

Key Concepts ◽

Basic Understanding ◽

Visualization Tools

We propose an exercise with the purpose of providing a basic understanding of key concepts within AI and extending the understanding of AI beyond mathematics. The exercise allows participants to carry out analysis based on accounting data using visualization tools as well as to develop their own machine learning algorithms that can mimic their decisions. Finally, we also problematize the use of AI in decision-making, with such aspects as biases in data and/or ethical concerns.

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A Study on Deep Learning Methods in the Concept of Digital Industry 4.0

Handbook of Research on Management and Strategies for Digital Enterprise Transformation - Advances in E-Business Research ◽

10.4018/978-1-7998-5015-1.ch016 ◽

2021 ◽

pp. 318-339

Author(s):

Mehmet Ali Şimşek ◽

Zeynep Orman

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Deep Learning ◽

Industry 4.0 ◽

Production Efficiency ◽

Learning Technologies ◽

High Potential ◽

Data Sets ◽

Learning Methods ◽

Intelligence Studies

Nowadays, the main features of Industry 4.0 are interpreted to the ability of machines to communicate with each other and with a system, increasing the production efficiency and development of the decision-making mechanisms of robots. In these cases, new analytical algorithms of Industry 4.0 are needed. By using deep learning technologies, various industrial challenging problems in Industry 4.0 can be solved. Deep learning provides algorithms that can give better results on datasets owing to hidden layers. In this chapter, deep learning methods used in Industry 4.0 are examined and explained. In addition, data sets, metrics, methods, and tools used in the previous studies are explained. This study can lead to artificial intelligence studies with high potential to accelerate the implementation of Industry 4.0. Therefore, the authors believe that it will be very useful for researchers and practitioners who want to do research on this topic.

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Ethical Management of Artificial Intelligence

Sustainability ◽

10.3390/su13041974 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1974

Author(s):

Alfred Benedikt Brendel ◽

Milad Mirbabaie ◽

Tim-Benjamin Lembcke ◽

Lennart Hofeditz

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Future Research ◽

Ethical Considerations ◽

Managerial Decision ◽

Ethical Management ◽

Ethical Concerns ◽

Starting Point ◽

Limited Support ◽

Ai Ethics

With artificial intelligence (AI) becoming increasingly capable of handling highly complex tasks, many AI-enabled products and services are granted a higher autonomy of decision-making, potentially exercising diverse influences on individuals and societies. While organizations and researchers have repeatedly shown the blessings of AI for humanity, serious AI-related abuses and incidents have raised pressing ethical concerns. Consequently, researchers from different disciplines widely acknowledge an ethical discourse on AI. However, managers—eager to spark ethical considerations throughout their organizations—receive limited support on how they may establish and manage AI ethics. Although research is concerned with technological-related ethics in organizations, research on the ethical management of AI is limited. Against this background, the goals of this article are to provide a starting point for research on AI-related ethical concerns and to highlight future research opportunities. We propose an ethical management of AI (EMMA) framework, focusing on three perspectives: managerial decision making, ethical considerations, and macro- as well as micro-environmental dimensions. With the EMMA framework, we provide researchers with a starting point to address the managing the ethical aspects of AI.

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Measuring Accounting Professionals Perception on use of AI Based Accounting Practices in India

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c5539.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 1854-1861

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Regression Method ◽

Accounting Profession ◽

Managerial Decision ◽

Managerial Decision Making ◽

Accounting Professionals ◽

Human Skills ◽

New Challenges ◽

Spss Software

Accounting as a progressive domain of knowledge is now ready to adapt new changes and understand how to effectively respond. Artificial intelligence (AI) has brought new challenges and solutions of old problems. It is intense technology not for replacing people but for improving importance of purely human skills like enthusiasm, creativity or empathy: all essential aspects of profession. AI is used for enhancing the human experience for decision making. This means deleting the monotonous work out from employee’s schedule and converting their skills towards managerial decision making. It deals with Large volumes of information that previously used to be succeeded by workforces are now controlled by AI while they can contentedly examine it. Composite altering patterns can be accustomed very easily in the data. These arrangements are extremely dependable than the previously tracked techniques. This research paper analyses measures the use of AI in accounting, auditing and recruiting with measuring its benefits and challenges. For this purpose a sample of 104 accounting professionals were taken and analysed by using regression method with SPSS software and revealed the hidden potentials of AI in the area of accounting profession.

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Machine Learning for All – Introducing Machine Learning in K-12

10.31235/osf.io/wj5ne ◽

2020 ◽

Author(s):

Christiane Gresse von Wangenheim ◽

Lívia S. Marques ◽

Jean C. R. Hauck

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Neural Networks ◽

Brazilian Portuguese ◽

Computing Education ◽

Ethical Concerns ◽

Introductory Course ◽

New Challenges ◽

K 12 ◽

Science Classes

Although Machine Learning (ML) is integrated today into various aspects of our lives, few understand the technology behind it. This presents new challenges to extend computing education early on including ML concepts in order to help students to understand its potential and limits and empowering them to become creators of intelligent solutions. Therefore, we developed an introductory course to teach basic ML concepts, such as fundamentals of neural networks, learning as well as limitations and ethical concerns in alignment with the K-12 Guidelines for Artificial Intelligence. It also teaches the application of these concepts, by guiding the students to develop a first image recognition model of recycling trash using Google Teachable Machine. In order to promote ML education, the interactive course is available online in Brazilian Portuguese to be used as an extracurricular course or in an interdisciplinary way as part of science classes covering recycling topics.

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Knowledge Mapping of Machine Learning Approaches Applied in Agricultural Management—A Scientometric Review with CiteSpace

Sustainability ◽

10.3390/su13147662 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7662

Author(s):

Jingyi Zhang ◽

Jiaxin Liu ◽

Yaqi Chen ◽

Xiaochun Feng ◽

Zilai Sun

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Making ◽

Agricultural Management ◽

Agricultural Science ◽

Learning Technology ◽

Learning Approaches ◽

Product Price ◽

Learning Methods ◽

Machine Learning Methods

With the continuous development of the Internet of Things, artificial intelligence, big data technology, and intelligent agriculture have become hot topics in agricultural science and technology research. Machine learning is one of the core topics in artificial intelligence, and its application has penetrated every aspect of human social life. In modern agricultural intelligent management and decision making, machine learning plays an important role in crop classification, crop disease and insect pest prediction, agricultural product price prediction, and other aspects of management and decision-making processes in agriculture. To detect and recognize the latest research developing features in a quantitative and visual way, and based on machine learning methods in agricultural management, the authors of this paper used CiteSpace bibliometric methods to analyze relevant studies on the development process and hot spots. High-value references, productive authors, country and institution distributions, journal visualizations, research topics, and emerging trends were reviewed and analyzed. According to the keyword visualization and high-value references, machine learning approaches focus on sustainable agriculture, water resources, remote sensing, and machine learning methods. The research mainly focuses on six topics: learning technology, land environment, reference evapotranspiration, decision support systems for river geography, soil management, and winter wheat, while learning technology has been the most popular in recent years.

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