[논문]인공지능 알고리즘은 사람을 차별하는가?

오요한; 홍성욱

인공지능 알고리즘은 사람을 차별하는가?
Does Artificial Intelligence Algorithm Discriminate Certain Groups of Humans? 원문보기

科學技術學硏究 = Journal of science & technology studies, v.18 no.3, 2018년, pp.153 - 216

오요한 (RPI 과학기술학과) , 홍성욱 (서울대학교 과학사 및 과학철학 협동과정)

초록
AI-Helper

빅데이터에 근거하여 자동적인 의사결정을 내리는 알고리즘이 사회의 각종 영역에서 점차 널리 사용되고 있는 저변에는 알고리즘의 의사결정이 사회의 자원을 보다 효율적으로 분배하리라는 기대 뿐만 아니라 그 결정이 선입견, 편향, 자의적 판단 등이 개입될 수 있는 인간의 의사결정보다 더 공정한 결과를 낳으리라는 희망 또한 자리잡고 있다. 하지만 알고리즘 의사결정이 그 결정에 의해 영향 받는 이들을 공정하게 다루지 않는다는 주장이 여러 사례와 함께 거듭 제기되면서, 의사결정이 어떻게 절차화되었는지, 또한 특정한 의사결정을 공정하다고 판단하는 데에 어떤 요인이 고려되는지에 대한 근본적인 질문들이 새롭게 제기되고 있다. 본 논문은 사법, 치안, 국가 안보의 세 가지 알고리즘 활용 영역에서 차별의 문제가 제기되는 상황을 구체적으로 분석한 연구들을 검토함으로써, 인공지능 알고리즘이 과연 특정 집단의 인간을 차별하는지, 그리고 공정한 의사결정을 분별하는 기준은 무엇인지 살펴보고자 한다. 본격적인 검토에 앞서 데이터 마이닝 각 단계에서 의도적으로 그리고 비의도적으로 편향적인 결과가 산출될 수 있는 원인에는 무엇이 있는지를 살필 것이다. 결론에서는 이러한 이론적이고 실질적인 검토가 현대 한국 사회에 시사하는 바가 무엇인지 간추려 제시할 것이다.

Abstract ▼ AI-Helper

The contemporary practices of Big-Data based automated decision making algorithms are widely deployed not just because we expect algorithmic decision making might distribute social resources in a more efficient way but also because we hope algorithms might make fairer decisions than the ones humans make with their prejudice, bias, and arbitrary judgment. However, there are increasingly more claims that algorithmic decision making does not do justice to those who are affected by the outcome. These unfair examples bring about new important questions such as how decision making was translated into processes and which factors should be considered to constitute to fair decision making. This paper attempts to delve into a bunch of research which addressed three areas of algorithmic application: criminal justice, law enforcement, and national security. By doing so, it will address some questions about whether artificial intelligence algorithm discriminates certain groups of humans and what are the criteria of a fair decision making process. Prior to the review, factors in each stage of data mining that could, either deliberately or unintentionally, lead to discriminatory results will be discussed. This paper will conclude with implications of this theoretical and practical analysis for the contemporary Korean society.

주제어

질의응답

핵심어	질문	논문에서 추출한 답변
	알고리즘의 설명책임 법안의 목표는 무엇인가?	이 법안은 뉴욕시가 특별위원회를 구성해서 시에서 사용되는 알고리즘(algorithm)2)이 뉴욕 시민의 삶에 어떤 영향을 미치는지, 그리고 이런 알고리즘이 연령, 인종, 종교, 성별, 성적 지향, 시민권의 여부에 따라서 시민들을 차별하는지를 조사하는 것을 의무화했다. 법안을 발의한 바카는 이 법안의 목표가 알고리즘의 ʻʻ투명성(transparency)과 설명책임ʼʼ을 확립하는 데에 있다고 강조했다.3) 올해 1월부터 시행된 이 법안에 따라서 뉴욕시는 5월에 공무원, 학계, 법조계, 과학기술계의 전문가로 구성된 태스크포스를 발족시켰다.
	알고리즘의 설명책임 법안이 어떤 것을 의무화 하는가?	2017년 12월, 미국 뉴욕시의 시의원 제임스 바카(James Vacca)와 동료들은 소위 "알고리즘의 설명책임 법안" (algorithmic accountability bill)1)이라고 불리는 법안을 발의했다(Kirchner, 2017). 이 법안은 뉴욕시가 특별위원회를 구성해서 시에서 사용되는 알고리즘(algorithm)2)이 뉴욕 시민의 삶에 어떤 영향을 미치는지, 그리고 이런 알고리즘이 연령, 인종, 종교, 성별, 성적 지향, 시민권의 여부에 따라서 시민들을 차별하는지를 조사하는 것을 의무화했다. 법안을 발의한 바카는 이 법안의 목표가 알고리즘의 ʻʻ투명성(transparency)과 설명책임ʼʼ을 확립하는 데에 있다고 강조했다.
	자동화된 결정 시스템은 무엇인가?	법안이 명시한 알고리즘은 ʻʻ자동화된 결정 시스템ʼʼ(automated decision system)이다. 이것은 ʻʻ결정을 내리거나 도움을 주는 데 사용되는 머신러닝(machine learning), 데이터 프로세싱(data processing), 혹은 인공지능(artificial intelligence)의 기술에서 유래된 알고리즘을 포함하는 알고리즘들의 컴퓨터화된 구현ʼʼ으로 정의된다.5) 인공지능을 인간을 대신하여 자동적으로 결정을 내리거나, 혹은 인간의 결정을 도와주는 알고리즘으로 잠정적으로 정의한다면, 뉴욕시의 법안은 인공지능 알고리즘을 투명하고 설명책임 있는 것으로 만들기 위한 법안이라고 볼 수 있다.

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저자의 다른 논문 :

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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