• 한국노년학
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• v.39 no.4
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• pp.959-976
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• 2019

The purpose of this study was to understand socio-demographic factors related to older adults' participation patterns in lifelong education. For the purpose, this study used the raw data of 2017 Survey of the Living Conditions of the Elderly (SLCE) conducted by The Korea Institute for Health and Social Affairs. From the data of 10,073 older adults, their lifelong education participation, participating program types, participating organizations, and participating frequency were analyzed by their sex, age, educational level, household income, the longest job status, and health status. This study found that female, age of 70-74 and 75-79, educational levels of high school and higher, the longest job status of regular employees and unpaid family workers, and decent health status of older adults more participated in lifelong education. According to lifelong education program types, significant differences were found between education groups of middle school/lower and groups of high school/higher and between 1, 2 quintile income groups and 3, 4, 5 quintile income groups. In relation to the participating organizations, groups of 70 years and older, middle school and higher education level, under 3 quintile income, and poor health tended to participate in lifelong education at the elderly welfare center, senior citizens, and elderly classrooms. In terms of participation frequency, high school and college/higher than 0 year of school education, and regular workers than unpaid family workers were more frequently participated in lifelong education. This study showed the inequality in lifelong education participation according to older adults' demographic characteristics; finally, this study suggested necessary policies and academic discussions for future older adults' lifelong education.

• Journal of Environmental Health Sciences
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• v.43 no.1
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• pp.1-22
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• 2017

'The worstest environment disaster', 'World's first biocide massacre', 'Home-based Sewol ferry disaster' are all phrases attached to the recent humidifier disinfectant disaster. In the spring of 2011, four of 8 pregnant women including 1 adult man passed away at a university hospital in Seoul due to breathing failure. Epidemiologic investigation conducted by the Korean CDC soon revealed the inhalation of humidifier disinfectant, which had been widely used in Korea during the winter, to be responsible for the disease. As well as lung fibrosis hardening of the lungs, other diseases including asthma, rhinitis, skin disease, liver disease, fetal disease or cancers have been researched for their relation with exposure to the products. By February 9, 2017, 5,342 cases had registered for health problems and 1,131 of them were already dead (20.8% mortality rate). Based on studies by government agencies and a telephone survey of the general population by Seoul National University and civic groups, around 20% of the general public of Korea has used these products. Since the market release of the first product by SK Chemical in 1994, over 7.1 million items from around 20 brands were sold up to 2011. Most of the products were manufactured by well-known large conglomerates such as SK, Lotte, Samsung, Shinsegye, LG, and GS, as well as some European companies including UK-based Reckitt Benckiser and TESCO, the German firm Henkel, the Danish firm KeTox, and an Irish company. Even though this disaster was unveiled in 2011 by the Korean government, the issue of the victims was neglected for over five years. In 2016, an unexpected but intensive investigation by prosecutors found that Reckitt Benckiser manipulated and concealed animal tests for its own brand and brought several university experts and company employees to court. The matter was an intense social issue in Korea from May to June with a surge in media coverage. The prosecutor's investigation and a nationwide boycott campaign organized by victims and environmental groups against Reckitt Benckiser, whose product had been used by more than 70% of victims, led to the producer's official apology and a compensation scheme. A legislative investigation organized after the April 2016 national election revealed the producers' faults and the government's responsibility, but failed to meet expectations. A special law for the victims passed the National Assembly in January 2017 and a punitive system together with a massive environmental epidemiology investigation are expected to be the only solutions for this tragedy. Sciences of medicine, toxicology and environmental health have provided decisive evidence so far, but for the remaining problems the perspectives of social sciences such as sociology and jurisprudence are highly necessary, similar to with the Minamata disease and Wonjin Rayon events. It may not be easy to follow this issue using unfamiliar terminology from medical and chemical science and the long, complicated history of the event. For these reasons the author has attempted to write this article in a question and answer format to render it easier to follow. The 17 questions are: Q1 What is humidifier disinfectant? Q2 What kind of health problems are caused by humidifier disinfectant? Q3 How many victims are there? Q4 What is the analysis of the 1,112 cases of death? Q5 What is the problem with the government's diagnostic criteria and the solution? Q6 Who made what brands? Q7 Has there been a recall? What is still on sale? Q8 Was safety not checked by any producers? Q9 What are the government's responsibilities? Q10 Is it true that these products were sold only in Korea? Q11 Why and how was it unveiled only in 2011 after 17 years of sales? Q12 What delayed the resolution of the victim issue? Q13 What is the background of the prosecutor's investigation in early 2016? Q14 Is it possible to report new victim cases without evidence of product purchase? Q15 What is happening with the victim issue? Q16 How does it compare with the cases of Minamata disease and Wonjin Rayon? Q17 Are there prevention measures and lessons?

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.