Informatization Policy (정보화정책)

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Analyzing employment trends in response to AI exposure: K-shaped labor polarization in Korea

인공지능 노출 정도에 따른 고용 추세 분석: K자형 고용 양극화

  • Lee, Yeseul (Graduate School of Data Science, Kyungpook National University ) ;
  • Hwang, Hyeonjun (Graduate School of Data Science, Kyungpook National University )
  • Received : 2023.03.16
  • Accepted : 2023.05.30
  • Published : 2023.09.30
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Abstract

The impact of technological advancements on employment is a matter of ongoing debate, with discussions on the effects of AI technology development on employment being particularly scarce. This study employs the natural language processing technique (SBERT) and patents to calculate an occupation-based AI exposure score and to analyze employment trends by group. It proposes a method for calculating the AI exposure score based on the similarity between Korean patent information and US job descriptions and linking SOC(U.S.) and KSCO(Korea). The analysis of domestic AI patent applications and regional employment data in the KOSIS Database since 2013 reveals a K-shaped polarization pattern in Korean employment trends among groups with above and below average levels of AI exposure.

기술 발전이 고용에 미치는 영향은 자동화에 의한 대체 또는 새로운 업무 도입에 따른 고용 증가 등 여전한 논쟁의 대상이다. 특히 인공지능 기술 발전과 고용에 대한 실증 논의는 더욱 부족한 실정이다. 이에 본 연구는 자연어처리 기법(SBERT)과 특허를 이용하여 직업별 인공지능 노출 점수를 계산하고 평균 점수를 기준으로 상위 집단과 하위 집단으로 구분하여 집단별 고용 추세를 분석한다. 자연어처리 기법을 통해 한국 특허와 미국 직업의 업무 설명을 연계하는 인공지능 노출 점수 계산 방식과 한미 표준직업분류 연계 방식을 제시하고 이를 국내 고용 통계에 적용하여 추세를 분석한다. 2013년 이후 국내 인공지능 출원 특허와 통계청 지역별고용조사를 분석한 결과 한국의 고용은 시간이 지남에 따라 평균 이상의 인공지능 노출 집단에서 우상향하고, 평균 이하 집단에서는 우하향하는 K자형 양극화 양상을 보인다.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2023-00242528).

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