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The Korean Society for Journalism & Communication (KSJCS)
[ Article ]
Korean Journal of Journalism & Communication Studies - Vol. 68, No. 4, pp.119-157
ISSN: 2586-7369 (Online)
Print publication date 31 Aug 2024
Received 02 Feb 2024 Accepted 29 Jul 2024 Revised 02 Aug 2024
DOI: https://doi.org/10.20879/kjjcs.2024.68.4.004

인공지능 팩트체크와 ‘사실성’의 기술사회적 정의

이정현** ; 박소영***
**중앙대학교 인문콘텐츠연구소 HK연구교수 maryjlee1205@gmail.com
***조선대학교 미디어커뮤니케이션학과 조교수 sy.park@chosun.ac.kr
Artificial Intelligence Fact-checking Technology and the Sociotechnical Definition of ‘Factuality’
Jeonghyun Lee** ; Soyoung Park***
**Research Professor, Humanities Research Institute at Chung-Ang University maryjlee1205@gmail.com
***Assistant Professor, Chosun University sy.park@chosun.ac.kr

초록

광범위하게 생산되고 빠르게 유통되는 인터넷 상의 오정보, 허위조작정보, 유해정보에 대한 대응책으로서 인공지능 기술을 통해 팩트체크를 자동화하려는 움직임이 지속되고 있다. 하지만 이는 팩트체크 과정 안에서 인공지능이 실행, 배치되는 조건이나 방식을 구체적으로 고찰한 후에 그 안에서 사실성이 협상되고 구성되는 방식을 이해하는 과정이 선행되지 않은 상태에서 기술 중심적인 해결책으로서 인공지능 기술이 제안되는 양상을 보인다. 이에 문제의식을 갖고 이 논문은 팩트체크 ‘기술’로서 인공지능이 ‘사실’을 구성하는 방식을 국내외 인공지능 팩트체크 기술사례를 통해 검토함으로써 인공지능 팩트체크가 구성하는 ‘사실성’이 사실을 더함으로써 사회적 실재를 구성하던 팩트체크 저널리즘과 어떻게 다른지 검토한다. 이를 위해 2023년 10월 기준 국제 팩트체킹 네트워크(International Fact-Checking Network, IFCN)에 의해 인증 받은 세계 팩트체크 인증기관들이 발표한 인공지능 팩트체크 기술과 세계적인 싱크탱크인 미국의 랜드 연구소(Rand Institute)에서 허위조작정보에 대한 대항(fighting disinformation)할 목적으로 구축한 온라인 데이터베이스에서 발표한 인공지능 팩트체크 기술 사례들을 검토하고, 각 기술 사례들의 목적과 ‘팩트 체크’ 근거에 따라 1) 주장의 탐지, 2) 증거 추출과 주장의 진실성 검증, 3) 정보 확산 형태의 탐지와 통제로 유형화했다. 이어서 각 유형이 저널리즘이 구축해 온 ‘사실성’을 어떻게 기술사회적으로 구성하는지 비판적으로 검토했다. 요약하면, 인공지능 팩트체크는 외부세계에 존재하는 ‘객관적 사실’이나 그 요건을 사전적, 기술적으로 규정하고 해당 요건에 맞지 않거나 맞는 것을 걸러내고 대조하는 과정을 자동화하여 궁극적으로는 기술이 구성할 수 있는 ‘사실의 요건’ 안에서 사실 자체를 재구성한다. 이 연구는 인공지능 기술의 도입 여부나 가능성 자체에 집중하고 있는 지금 상황에서 벗어나 팩트체크의 맥락 안에서 인공지능이 실행, 배치되는 조건과 방식을 구체적으로 살펴볼 필요가 있음을 강조하고. 팩트체크의 복잡한 과정 중에 인공지능에 의해 자동화되는 것이 무엇인가에 따라 ‘가짜뉴스’가 지칭하는 것은 매우 느슨하게 정의되며 저널리즘에서 강조해 온 사실성 역시 정치적으로 (재)정의될 수 있음을 기술 사례 분석을 통해 드러내고 있다.

Abstract

There has been a continuous movement to automate fact-checking through artificial intelligence (AI) technology as a countermeasure to the widespread production and rapid dissemination of misinformation, disinformation, and harmful information on the internet. However, this approach often appears to be a technology-centric solution with lacking two key perspectives. First, it proposes AI in the fact-checking process without sufficient consideration of the conditions and methods for AI implementation in the fact-checking process. Second, the current approach lacks an understanding of how factuality is negotiated and constructed within fact-checking process performed by AI. This paper addresses these issues by examining how AI, as a ‘technology’ of fact-checking, constructs ‘facts’ through domestic and international AI fact-checking technology cases. This paper explores how the ‘factuality’ constructed by AI fact-checking differs from the ‘factuality’ provided by traditional fact-checking journalism, which has historically shaped social reality by verifying and adding facts. To achieve this, the paper reviews AI fact-checking technologies presented by globally certified fact-checking organizations as of October 2023. The sources come from the International Fact-Checking Network (IFCN) and AI fact-checking technology cases published in an online database created by the Rand Institute in the U.S., aimed at fighting disinformation. Based on the objectives of each technology and how they are set up, this paper categorizes the AI fack-checking technology cases into: 1) detection of claims, 2) evidence extraction and verification of claim truthfulness, and 3) detection and control of information dissemination patterns. This paper then critically examines how each type of technology socially constructs ‘factuality’ compared to the ways in which the traditional journalism approach has defined ‘factuality.’ In sum, AI fact-checking involves the automation of the process through which external ‘objective facts' and their requirements are defined in technically sensible ways, and the machine ‘filters’ and ‘matches’ information based on whether it meets these criteria. Ultimately, this process reconstructs the nature of facts within the ‘requirements of facts' that the technology can construct. This study emphasizes the need to move beyond the current focus on whether AI technology can be adopted or implemented, and instead calls for a detailed examination of the specific conditions and methods under which AI is deployed within the context of fact-checking. This study reveals, through the analysis of case studies, that the definition of ‘fake news’ becomes highly ambiguous, depending on which aspects of the complex fact-checking process are automated by AI, and that the concept of 'factuality,' traditionally emphasized in journalism, can also be subject to political (re)definition.

Keywords:

Fact-check, Artificial Intelligence, Factuality, Fake News, Journalism

키워드:

팩트체크, 인공지능, 사실성, 가짜뉴스, 저널리즘

Acknowledgments

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(이 논문은 2017년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임) [NRF-2017S1A6A3A01078538].

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