• Journal of Industrial Convergence
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• v.20 no.5
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• pp.83-92
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• 2022

As cases of social and ethical problems caused by artificial intelligence technology have occurred, artificial intelligence ethics are drawing attention along with social interest in the risks and side effects of artificial intelligence. Artificial intelligence ethics should not just be known and felt, but should be actionable and practiced. Therefore, this study proposes an artificial intelligence ethics education model to strengthen the practical ability of artificial intelligence ethics. The artificial intelligence ethics education model derived educational goals and problem-solving processes using artificial intelligence through existing research analysis, applied teaching and learning methods to strengthen practical skills, and compared and analyzed the existing artificial intelligence education model. The artificial intelligence ethics education model proposed in this paper aims to cultivate computing thinking skills and strengthen the practical ability of artificial intelligence ethics. To this end, the problem-solving process using artificial intelligence was presented in six stages, and artificial intelligence ethical factors reflecting the characteristics of artificial intelligence were derived and applied to the problem-solving process. In addition, it was designed to unconsciously check the ethical standards of artificial intelligence through preand post-evaluation of artificial intelligence ethics and apply learner-centered education and learning methods to make learners' ethical practices a habit. The artificial intelligence ethics education model developed through this study is expected to be artificial intelligence education that leads to practice by developing computing thinking skills.

• Korean Journal of Artificial Intelligence
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• v.7 no.1
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• pp.17-22
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• 2019

The rapid development of artificial intelligence technology has exerted a great influence on all fields of the world, which of course also affects the world economy. This has also aroused a large number of economists' interest in this proposition. Since the definition of artificial intelligence is not unified yet, the results from previous researches are not reliable enough. At present, most scholars use the neoclassical growth model or task-based model to explore the path of artificial intelligence on economic variables. There into, most of them use the degree of automation to represent the artificial intelligence. They find that the degree of automation can change the proportion of industries. This only verifies that artificial intelligence can affect the economic variables. But the magnitude of artificial intelligence on economic variables can not be correctly estimated. Therefore, in order to have a better understanding on the impact of artificial intelligence on economic growth, this paper systematically reviews and collates previous literature on this topic. The results of this paper indicate that both in theoretical and empirical studies, artificial intelligence has a positive effect on economic growth. Then, some suggestions and limitations have also been put forward accordingly.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.349-354
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• 2021

In this study, a model that can converge with artificial intelligence in each subject as software and artificial intelligence education become mandatory in the curriculum revised in 2022 is proposed. The proposed AI convergence education model is based on the content of the subject (accomplishment standard + subject). The second axis is artificial intelligence tools, the third axis is artificial intelligence technology, and the fourth axis is data applied in daily life. In order to apply artificial intelligence to each subject, it is necessary to apply artificial intelligence tools, artificial intelligence technology, and data in daily life to the achievement standards and content of each subject. If the achievement standards and subject contents are structured in this way, it can be seen that the convergence with each subject is good. Therefore, when composing textbooks by achievement standards and topics, it is necessary to add artificial intelligence tools, artificial intelligence technology, and daily data.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.187-195
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• 2023

This study propose to develop artificial intelligence liberal arts courses for college students in the humanities and social sciences majors using the entry artificial intelligence model. A group of experts in computer, artificial intelligence, and pedagogy was formed, and the final artificial intelligence liberal arts course was developed using previous research analysis and Delphi techniques. As a result of the study, the educational topics were largely composed of four categories: image classification, image recognition, text classification, and sound classification. The training consisted of 1) Understanding the principles of artificial intelligence, 2) Practice using the entry artificial intelligence model, 3) Identifying the Ethical Impact, and 4) Based on learned, team idea meeting to solve real-life problems. Through this course, understanding the principles of the core technology of artificial intelligence can be directly implemented through the entry artificial intelligence model, and furthermore, based on the experience of solving various real-life problems with artificial intelligence, and it can be expected to contribute positively to understanding technology, exploring the ethics needed in the artificial intelligence era.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.1-14
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• 2024

While text-to-image models have made remarkable progress in image synthesis, certain models, particularly generative diffusion models, have exhibited a noticeable bias to- wards generating images related to the culture of some developing countries. This paper introduces an empirical investigation aimed at mitigating the bias of image generative model. We achieve this by incorporating symbols representing Saudi culture into a stable diffusion model using the Dreambooth technique. CLIP score metric is used to assess the outcomes in this study. This paper also explores the impact of varying parameters for instance the quantity of training images and the learning rate. The findings reveal a substantial reduction in bias-related concerns and propose an innovative metric for evaluating cultural relevance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2334-2347
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• 2023

Metaverse services generate text data, data of ubiquitous computing, in real-time to analyze user emotions. Analysis of user emotions is an important task in metaverse services. This study aims to classify user sentiments using deep learning and pre-trained language models based on the transformer structure. Previous studies collected data from a single platform, whereas the current study incorporated the review data as "Metaverse" keyword from the YouTube and Google Play Store platforms for general utilization. As a result, the Bidirectional Encoder Representations from Transformers (BERT) and Robustly optimized BERT approach (RoBERTa) models using the soft voting mechanism achieved a highest accuracy of 88.57%. In addition, the area under the curve (AUC) score of the ensemble model comprising RoBERTa, BERT, and A Lite BERT (ALBERT) was 0.9458. The results demonstrate that the ensemble combined with the RoBERTa model exhibits good performance. Therefore, the RoBERTa model can be applied on platforms that provide metaverse services. The findings contribute to the advancement of natural language processing techniques in metaverse services, which are increasingly important in digital platforms and virtual environments. Overall, this study provides empirical evidence that sentiment analysis using deep learning and pre-trained language models is a promising approach to improving user experiences in metaverse services.

• Korean Journal of Artificial Intelligence
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• v.7 no.2
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• pp.9-12
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• 2019

With the Chinese government's attention to the artificial intelligence industry, the Chinese government has invested a lot in it recently. Of course, the importance of artificial intelligence industry for China's economic development is increasingly significant. The advent of artificial intelligence boom has also triggered a large number of scientists to analyze the impact of artificial intelligence on economic growth. Therefore, this paper use 31 China's cross-province panel data to study the effect of artificial intelligence on economic growth. Via empirical analyses under a series of econometric methods such as the province and year fixed effect model, the empirical result shows that artificial intelligence has a positive and significant effect on economic growth. Namely, the artificial intelligence is a new engine for economic growth. Meanwhile, the empirical results also indicate that the investment and consumption has a significant and positive effect on economic growth. Oppositely, the inflation and government purchase have a significant negative effect on economic growth. These findings in this paper also provide some important evidences for policy-makers to perform precise behaviors so as to promote the economic growth. Moreover, these finding enriches existing literature on artificial intelligence and economic growth.

• Journal of The Korean Association of Information Education
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• v.21 no.1
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• pp.139-149
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• 2017

Artificial intelligence education is very important in the 21st century knowledge information society. Even if it is very important to understand artificial intelligence and practice computer programming in computer education in the fourth industrial revolution, but there is no teaching and learning model to understand artificial intelligence and computer programming education. In this paper, the proposed model consists of problem understanding step, data organizing step, artificial intelligence model setting step, programming step, and report writing step. At the program step, students can choose to copy, transform, create, and challenge steps to their level. In this study, the validity of the model was proved by the Delphi evaluation of elementary school teachers. The results of this study provide a good opportunity for elementary school students to practice artificial intelligence programs.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.142-147
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• 2021

In this paper, we propose an intelligent research fund management system that applies artificial intelligence technology to an integrated research fund management system. By defining research fund management rules as work rules, a detection model learned using deep learning is designed, through which the disapproval status is presented for each research fund usage history. The disapproval detection system of the RCMS implemented in this study predicts whether the newly registered usage details are recognized or disapproved using an artificial intelligence model designed based on the use of an 8.87 million research fund registered in the RCMS. In addition, the item-detail recommendation system described herein presents the usage details according to the usage history item newly registered by the artificial intelligence model through a correlation between the research cost usage details and the item itself. The accuracy of the recommendation was shown to be 97.21%.

• Knowledge Management Research
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• v.19 no.1
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• pp.97-118
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• 2018

The finance-investment industry is currently focusing on research related to artificial intelligence and big data, moving beyond conventional theories of financial engineering. However, the case of equity optimization portfolio by using an artificial intelligence, big data, and its performance is rarely realized in practice. Thus, the purpose of this study is to propose process improvements in equity selection, information analysis, and portfolio composition, and lastly an improvement in portfolio returns, with the case of an equity optimization model based on quantitative research by an artificial intelligence. This paper is an empirical study of the portfolio based on an artificial intelligence technology of "D" asset management, which is the largest domestic active-quant-fiduciary management in accordance with the purpose of this paper. This study will apply artificial intelligence to finance, analyzing financial and demand-supply information and automating factor-selection and weight of equity through machine learning based on the artificial neural network. Also, the learning the process for the composition of portfolio optimization and its performance by applying genetic algorithms to models will be documented. This study posits a model that the asset management industry can achieve, with continuous and stable excess performance, low costs and high efficiency in the process of investment.