Governments around the world are enacting laws mandating explainable traceability when using AI(Artificial Intelligence) to solve real-world problems. HAI(Human-Centric Artificial Intelligence) is an approach that induces human decision-making through Human-AI collaboration. This research presents a case study that implements the Human-AI collaboration to achieve explainable traceability in governmental data analysis. The Human-AI collaboration explored in this study performs AI inferences for generating labels, followed by AI interpretation to make results more explainable and traceable. The study utilized an example dataset from the Ministry of Oceans and Fisheries to reproduce the Human-AI collaboration process used in actual policy-making, in which the Ministry of Science and ICT utilized R&D PIE(R&D Platform for Investment and Evaluation) to build a government investment portfolio.

1. 서 론
2. 선행연구
3. 연구방법
    3.1 분석 데이터
    3.2 LDA 토픽모델링
    3.3 기계학습 및 해석 방법론
    3.4 동시출현 네트워크
4. 분석 및 결과
5. 결 론
cknowledgement
References

• Jung Sun Lim(KISTI) | 임정선 (한국과학기술정보연구원)
• Seoung Hun Bae(LXSIRI) | 배성훈 (한국국토정보공사 공간정보연구원)
• Kil-Ho Ryu(KNSU) | 류길호 (한국체육대학교)
• Sang-Gook Kim(KISTI) | 김상국 (한국과학기술정보연구원) Corresponding author