• Smart Media Journal
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• v.13 no.1
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• pp.67-75
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• 2024

The purpose of this research is to investigate consumer responses to production recommendations by AI. In order to test hypotheses of this study, we conducted experimental study that was a 2(Brand reputation: high vs. low) X 2(Social comparison: high vs. low). The results of this study showed the interaction effects of brand reputation and social comparison on brand attitude. Based on the results, we provide theoretical implications to extent the existing research regarding product recommendations. Moreover, the results of this study provide some practical implications and a new aspect about AI recommendations.

• Asia pacific journal of information systems
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• v.29 no.2
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• pp.203-216
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• 2019

There is an accuracy-diversity dilemma with personalized recommendation services. Some researchers believe that accurate recommendations might reinforce customer satisfaction. However, others claim that highly accurate recommendations and customer satisfaction are not always correlated. Thus, this study attempts to establish the causal factors that determine customer satisfaction with personalized recommendation services to reconcile these incompatible views. This paper employs statistical analyses of simulation to investigate an accuracy-diversity dilemma with personalized recommendation services. To this end, we develop a personalized recommendation system and measured accuracy, diversity, and customer satisfaction using a simulation method. The results show that accurate recommendations positively affected customer satisfaction, whereas diverse recommendations negatively affected customer satisfaction. Also, customer satisfaction was associated with the recommendation product size when neighborhood size was optimal in accuracy. Thus, these results offer insights into personalizing recommendation service providers. The providers must identify customers' preferences correctly and suggest more accurate recommendations. Furthermore, accuracy is not always improved as the number of product recommendation increases. Accordingly, providers must propose adequate number of product recommendation.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.11-23
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• 2022

Although artificial intelligence(AI) is a key technology in the digital transformation among the emerging technologies, there are concerns about the use of AI, so many countries have been trying to set up a proper regulation system. This study analyzes the cases of the regulation policies on AI in USA, EU and Korea with the aim to set up and improve proper AI policies and strategies in Korea. In USA, the establishment of the code of ethics for the use of AI is led by private sector. On the other side, Europe is strengthening competitiveness in the AI industry by consolidating regulations that are dispersed by EU members. Korea has also prepared and promoted policies for AI ethics, copyright and privacy protection at the national level and trying to change to a negative regulation system and improve regulations to close the gap between the leading countries and Korea in AI. Moreover, this study analyzed the course of policy changes of AI regulation policy centered on ACF(Advocacy Coalition Framework) model of Sabatier. Through this study, it proposes hyper-scale AI regulation policy recommendations for improving competitiveness and commercialization in Korea. This study is significant in that it can contribute to increasing the predictability of policy makers who have difficulties due to uncertainty and ambiguity in establishing regulatory policies caused by the emergence of hyper-scale artificial intelligence.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.227-239
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• 2023

Online marketing is a rapidly growing industry that heavily depends on digital technologies and data analysis to effectively reach and engage consumers. For that, artificial intelligence (AI) has emerged as a crucial tool for online marketers, enabling marketers to analyze extensive consumer data and automate decision-making processes. The purpose of this study was to investigate the ethical implications of using AI in online marketing, focusing on its impact on consumer privacy and decision-making. AI has created new possibilities for personalized marketing but raises concerns about the collection and use of consumer data, transparency and accountability of decision-making, and the impact on consumer autonomy and privacy. In this study, we reviewed the relevant literature and case studies to assess the potential risks and make recommendations for improving consumer protection. The findings provide insights into ethical considerations and offer a roadmap for balancing the advantages of AI in online marketing with the protection of consumer rights. Companies should consider these ethical issues when implementing AI in their marketing strategies. In this study, we explored the concerns and provided insights into the challenges posed by AI in online marketing, such as the collection and use of consumer data, transparency, and accountability of decision-making, and the impact on consumer autonomy and privacy.

• The Korean Journal of Food & Health Convergence
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• v.10 no.1
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• pp.1-6
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• 2024

Purpose: The research aims to address the intricacies of AI and Big Data application within the food industry. This study explores the strategic implementation of AI and Big Data in the food industry. The study seeks to understand how these technologies can be employed to bolster consumer engagement and contribute to market expansion, while considering ethical implications. Research Method: This research employs a comprehensive approach, analyzing current trends, case studies, and existing academic literature. It focuses on the application of AI and Big Data in areas such as supply chain management, consumer behavior analysis, and personalized marketing strategies. Results: The study finds that AI and Big Data significantly enhance market analytics, consumer personalization, and market trend prediction. It highlights the potential of these technologies in creating more efficient supply chains, improving consumer satisfaction through personalization, and providing valuable market insights. Conclusion and Implications: The paper offers actionable insights and recommendations for the effective implementation of AI and Big Data strategies in the food industry. It emphasizes the need for ethical considerations, particularly in data privacy and the transparency of AI algorithms. The study also explores future trends, suggesting that AI and Big Data will continue to revolutionize the industry, emphasizing sustainability, efficiency, and consumer-centric practices.

• Knowledge Management Research
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• v.25 no.3
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• pp.173-200
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• 2024

This study investigates the dynamics of the digital divide and social inclusion in a society increasingly influenced by artificial intelligence (AI) by 2035. Using a 2×2 matrix scenario analysis, the research explores future scenarios based on two axes: the level of AI technological advancement and societal response. The scenarios range from an "Inclusive AI Society," characterized by advanced AI technology and comprehensive societal measures, to an "AI Polarized Society," marked by rapid AI advancement but fragmented social responses, exacerbating inequalities. The study emphasizes the critical role of both technological and social strategies in addressing the challenges of AI-driven societies. It provides policy recommendations to mitigate potential disparities, highlighting the need for inclusive education, equitable access to AI benefits, and adaptive governance frameworks. The findings aim to inform policymakers and stakeholders about the impacts of AI on social inclusion and the digital divide, proposing strategies for fostering a balanced and equitable AI future.

• Korean Journal of Artificial Intelligence
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• v.12 no.3
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• pp.25-31
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• 2024

The purpose of this study is to develop a sophisticated web-based artificial intelligence chatbot system designed to provide personalized dietary service for diabetic patients. According to a 2022 study, the prevalence of diabetes among individuals over 30 years old was 15.6% in 2020, identifying it as a significant societal issue with an increasing patient population. This study uses generative AI algorithms to tailor dietary recommendations for the elderly and various social classes, contributing to the maintenance of healthy eating habits and disease prevention. Through meticulous fine-tuning, the learning loss of the AI model was significantly reduced, nearing zero, demonstrating the chatbot's potential to offer precise dietary suggestions based on calorie intake and seasonal variations. As this technology adapts to diverse health conditions, ongoing research is crucial to enhance the accessibility of dietary information for the elderly, thereby promoting healthy eating practices and supporting disease prevention.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.633-640
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• 2024

This study develops AI-based 3D body shape modeling technology that can be utilized in the healthcare sector, proposing a system that enables monitoring of users' body shape changes and health status. Utilizing data from Size Korea, the study developed a model to generate 3D body shape images from 2D images, and compared various models to select the one with the best performance. Ultimately, by proposing a system process through the developed technology, including personalized health management, exercise recommendations, and dietary suggestions, the study aims to contribute to disease prevention and health promotion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.

• Nutritional Sciences
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• v.6 no.1
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• pp.48-52
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• 2003

The Dietary Reference Intakes (DRI's) are new nutrient intake standards that are being set for the United States and Canada. There are currently four types of DRI's: Estimated Average Requirements (EAR), Recommended Dietary Allowances (RDA), Adequate Intakes (AI), and Tolerable Upper Intake Levels (UL). The EAR is the nutrient intake that would be adequate for about half the population, while intake at the RDA should be adequate for 97-98% of the population. When the data are insufficient to set an EAR and RDA, then an AI is set. The UL is the highest intake level that does not pose a risk of adverse effects. The EAR, AI, and UL may be used to assess intakes of both individuals and of groups of people. For individuals, the EAR is used to calculate the probability that intake is inadequate, the AI is used to decide if the probability of inadequacy is low, and the UL is used to determine if a risk of excess intake is present. For groups. the EAR is used to estimate the prevalence of inadequacy, the AI is used to decide if the prevalence of inadequacy is low, and the UL is used to estimate the prevalence of excessive intakes. Because this approach to setting and applying nutrient standards is new, research recommendations include improving estimates of risk, improving dietary data, and improving statistical methods.