인공지능이 미래 고용에 미칠 영향 분석
Analyzing the impact of artificial intelligence on future employment
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
본 연구의 목적은 인공지능(AI)이 일자리 시장에 미치는 영향을 분석하고자 하였다. 이를 위해 구글에서 2017년 1월 1일부터 2024년 8월 31일까지 수집된 22,259건의 고용 관련 뉴스 데이터를 활용하여 AI와 고용의 관계를 탐구하였다. 연구 방법으로는 텍스트 마이닝을 기반으로 한 빈도 분석, 동적 주제 분석, 연관규칙 분석을 결합하여 AI가 일자리에 미치는 영향의 복잡한 경로를 식별하였다. 토픽 분석과 TF-IDF 분석을 통해 AI의 부정적 영향 ‘우려’, ‘직업 이동’, ‘도전’과 ‘긍정적 기회’, ‘새로운 기회’, ‘잠재력’에 대한 논의를 확인하였다. 또한 동적 토픽 모델링을 활용하여 시간에 따른 주제의 변화를 추적한 결과, 2019년까지는 주로 기업 내 기술 개발, 혁신, 기술 도구가 산업에 미치는 영향을 다루었고, 이후에는 자동화가 노동 시장에 미치는 영향과 근로자의 역할, 특히 기술 수요와 고용 기회의 변화를 조명하였다. 연관규칙 분석을 통해 AI가 일자리 경로에 미치는 영향을 신뢰도 지표를 기반으로 확인하였으며, 시장과 고용에 대한 복합적인 영향을 구체적으로 분석하였다.
Abstract
This study aimed to analyze the impact of artificial intelligence (AI) on the job market comprehensively. Utilizing 22,259 employment-related news articles collected from January 1, 2017, to August 31, 2024, we investigate the relationship between AI and employment. Our research methodology integrated text mining techniques, including frequency and dynamic topic analysis, along with association rule analysis, to identify the complex pathways through which AI influences job markets. Topic analysis and TF-IDF analysis highlighted discussions surrounding the negative impacts of AI, such as “concerns,” “job displacement,” and “challenges,” as well as the positive opportunities it presents, including “new opportunities” and “potential.” Dynamic topic modeling enabled us to trace the evolution of these themes over time, initially focusing on the influence of technological development and innovation within companies until 2019. Subsequently, the study illuminated the effects of automation on the labor market and examined the evolving roles of workers, particularly in response to changing demands for technology and shifts in employment opportunities. Through association rule analysis, we confirmed the significant impact of AI on job pathways based on reliability indicators and meticulously analyzed AI’s multifaceted effects on the market and employment.
참고문헌 (17)
- Clauberg, R., "Challenges of digitalization and artificial intelligence for modern economies, societies and management", RUDN Journal of Economics, 28(3), pp.556~567 (2020)[DOI]
- Frank, M. R., "Small cities face greater impact from automation", Journal of the Royal Society Interface, 15(139), pp.20170946~ (2018)[DOI]
- Saithibvongsa, P., "Artificial intelligence in the computer-age threatens human beings and working conditions at workplaces", Electronics Science Technology and Application, 5(3), pp.1~12 (2018)[DOI]
- Mann, K., "Benign effects of automation : New evidence from patent texts", SSRN 2959584, ~ (2018)
- Georgieff, A., "Artificial intelligence and employment : new crosscountry evidence", Frontiers in artificial intelligence, 5, pp.832736~ (2022)[DOI]
- Melemuku, S. A., "Artificial Intelligence and the Associated Threats on the Human Workforce", 10.31219, amnyq, ~
- Dijmărescu, I., "The future of work in a jobless society: Globalization, smart digitalization, and cognitive automation", SHS Web of Conferences, 92, pp.07016~ (2021), EDP Sciences
- Rickardo, G., "Artificial intelligence : Its impact on employability", World Journal of Advanced Research and Reviews, 18(3), pp.198~203 (2023)[DOI]
- Dietzmann, C., "The convergence of distributed ledger technology and artificial intelligence: An end-to-end reference lending process for financial services", ~ (2020)
- Xu, G., "The relationship of artificial intelligence opportunity perception and employee workplace well-being : A moderated mediation model", International Journal of Environmental Research and Public Health, 20(3), pp.1974~ (2023)[DOI]
- Moniz, A. B., "Changes in productivity and labour relations : artificial intelligence in the automotive sector in Portugal", International Journal of Automotive Technology and Management, 22(2), pp.222~244 (2022)[DOI]
- Jain, M., "Employability implications of artificial intelligence in healthcare ecosystem : responding with readiness", Foresight, 23(1), pp.73~94 (2021)[DOI]
- Blei, D. M., "Dynamic topic models", Proceedings of the 23rd international conference on Machine learning, pp.113~120 (2006)
- Agrawal, R., "Fast algorithms for mining association rules", Proc. 20th int. conf. very large data bases, VLDB (1994)
- Larose, D. T., "Discovering knowledge in data: an introduction to data mining (Vol. 4)" (2014), John Wiley & Sons
- Ampornphan, P., "Patent knowledge discovery using data analytics", Proceedings of the 2017 International Conference on Information Technology (2017)