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作者(中文):楊蕙亘
作者(外文):Yang, Hui-Hsuan
論文名稱(中文):演算法監控社會來臨?論公部門運用臉部辨識科技之管制
論文名稱(外文):The Age of Algorithmic State Surveillance? Regulating the Use of Facial Recognition Technology by the Public Sector
指導教授(中文):林勤富
指導教授(外文):Lin, Ching-Fu
口試委員(中文):李怡俐
莊弘鈺
口試委員(外文):Lee, Yi-Li
Chuang, Hung-Yu
學位類別:碩士
校院名稱:國立清華大學
系所名稱:科技法律研究所
學號:107074602
出版年(民國):111
畢業學年度:110
語文別:中文
論文頁數:144
中文關鍵詞:臉部辨識人工智慧演算法基本權利科技管制監控社會
外文關鍵詞:facial recognition technologyartificial intelligencealgorithmhuman rightstechnology governancesurveillance society
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從利用臉部辨識科技(FRT)驗證使用者身分以解鎖手機、邊境智慧化出入境管制、因應COVID-19持續擴散而有追蹤接觸者與確診者之需要,乃至作為警察為維護治安用以辨識犯罪者或嫌疑人的工具,抑或國家透過街道上安裝的監控鏡頭即時連續地辨識人群中的每個人,FRT已悄無聲息地擴展至與人們日常生活密切相關的私人或公共領域。FRT會因其運作流程與應用情境之不同而異其潛在風險程度,於公部門應用之不同情境下恐對言論自由與集會結社權利、正當程序權利以及隱私權等基本權利造成侵害。尤有甚者,若政府使用新興科技替代或與傳統工具結合作為新的管制手段,則除了促使現代監控國家的興起之外,FRT諸如演算法不透明性、權利侵害之可課責性、對少數民族和弱勢族群之錯誤再現(misrepresentation)、系統偏見或歧視之隱憂,亦會伴隨監控社會而使風險與不利影響進一步規模化。自2019年起,美國舊金山、波士頓、波特蘭等多個城市已禁止公部門使用FRT,而微軟、Amazon、IBM等大型科技公司亦相繼宣布不同的禁止政策。歐盟執委會亦於2021年4月21日發布《人工智慧法律一致性規則草案》(Proposal for a Regulation Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts),提出以風險區分為基礎的管制框架。儘管如此,中國作為主要FRT擁護者,除在中國境內將FRT用於社會信用系統等多種目的,更希望能進一步影響FRT在世界各國的使用。本文首先從FRT運作流程以及應用情境出發,以對相應潛在限制與風險進行剖析。接著,因應該限制與風險,嘗試勾勒出歐盟、美國與中國對於公部門使用FRT之管制框架發展及其對該國之影響。其後,針對不同管制考量、管制機構選擇、管制的強度與限制以及管制模式類型進行分析並提出本文結論,以對於我國FRT管制提供足資參考之建議。
Today, facial recognition technology (FRT) is everywhere. FRT can be found on your phone or at border control as a method of user identification. For example, it can be used by the public sector to conduct COVID-19 contact tracing and identify and track perpetrators or criminals to ensure public safety, or it can be installed as part of a public surveillance system. These different uses for FRT correspond to different levels of risk. In the public sector, although FRT has the potential to enhance public safety by monitoring and tracking crimes, it also threatens the general public’s freedom of speech, right to privacy, and due process rights. Additionally, existing concerns surrounding AI algorithms – such as algorithmic opacity, accountability, bias, and misrepresentation of marginalized groups – may be reiterated if FRT is widely utilized by the public sector. Since 2019, regulators have proposed or implemented various approaches to regulate FRT. Some cities in the United States, including San Francisco, Boston, and Portland, as well as several big tech companies, such as Microsoft, Amazon, and IBM, have announced different forms of bans or moratoriums on the use of FRT. In April 2021, the European Union (EU) introduced a governance framework for the use of FRT based on risk levels. On the other hand, China, an advocate for the use of FRT, has continued to expand the country’s use of FRT in its various systems while simultaneously influencing other countries to adopt it. This thesis seeks to analyze the reason behind the divergence in regulating FRT by observing regulatory approaches toward public sector use of FRT in the EU, the US, and China. This thesis will first provide an overview of the technical processes surrounding FRT, including limitations and risks of the technology. With the limitations and risks identified, this thesis will then provide an overview of how the EU, the US, and China mitigate such risks as regulators, including regulatory approach, choice of competent authority, and regulatory intensity. Finally, an analysis of the regulatory approaches to FRT will serve as a blueprint for this thesis to provide regulatory suggestions regarding how Taiwan can regulate the use of FRT by the public sector.
摘要 I
Abstract II
誌謝辭 III
目錄 IV
圖目錄 VIII
表目錄 VIII
第一章、緒論 1
第一節、問題意識 1
第二節、文獻回顧 9
第一項、對於社會監控的管制 9
第二項、對於FRT的管制 15
第一款、人權與基本權利面向 15
第二款、FRT演算法偏見與倫理面向 18
第三款、管制取徑面向 20
第三節、研究方法 24
第四節、研究範圍與限制 24
第五節、研究架構 27
第二章、臉部辨識科技之發展與應用風險 29
第一節、FRT之技術發展、類型與使用情境 29
第一項、FRT運作方式 29
第二項、FRT類型 30
第三項、FRT使用情境 31
第一款、邊境管理與交通安全 32
第二款、國內執法與犯罪偵查 32
第三款、醫學評估 33
第二節、FRT使用情境之潛在風險 34
第一項、基本權利之潛在侵害 34
第一款、言論自由與集會結社自由 34
第二款、正當法律程序權利 37
第三款、隱私權 39
第二項、社會控制之強化 40
第三項、FRT演算法本身之限制與風險 41
第一款、源於技術面向之FRT局限性 42
第二款、源於社會面向之FRT風險 45
第三節、小結 47
第三章、FRT管制模式:以歐盟、美國、中國為探討中心 49
第一節、歐盟對於FRT之管制取徑 51
第一項、一般性法律框架:個人資料、隱私保護等基本權利面向 52
第一款、FRT相關臉部影像處理基本原則 53
第二款、FRT相關臉部影像處理特定要求 55
第三款、特別制度設計:資料保護影響評估、事前諮詢主管監管機關 56
第二項、針對AI新興科技之管制嘗試 58
第二節、美國對於FRT之管制取徑 64
第一項、隱私保障與對政府監控之限制 65
第一款、隱私之憲法保障 65
第二款、對政府監控之法律限制 67
第二項、聯邦層級之FRT立法與管制要求 69
第三項、各州之FRT立法與管制要求 72
第一款、州FRT法規的共同要素 73
第二款、州FRT法規的獨特設計與要求 76
第三節、中國對於FRT之管制取徑 82
第一項、FRT於國家監控平臺之大規模應用 83
第二項、與FRT直接相關之管制 85
第一款、個人資訊保護為目的之法規 85
第二款、FRT技術標準 86
第三項、與FRT非直接相關之管制 87
第一款、個人資訊保護為目的之法規 87
第二款、網路治理為目的之立法 89
第四章、管制模式比較分析 91
第一節、管制目的與管制機構權責 91
第一項、基於人權與個人資訊權益考量 92
第一款、法院作為主要管制者 93
第二款、獨立機關作為主要管制者 94
第二項、基於新興科技風險控管考量 96
第一款、以第三方認證機構作為主要管制者 97
第二款、以資料庫主管機關作為主要管制者 97
第三項、基於公共安全與公共利益考量 99
第二節、管制模式類型化與介入時點 100
第一項、積極型管制模式 101
第一款、FRT應用前之事前管制 103
第二款、FRT應用中之事中管制 105
第三款、FRT應用後之事後管制 106
第二項、放任型管制模式 107
第五章、結論:我國管制現況與管制模式建議 110
第一節、我國FRT應用現況 110
第二節、我國FRT管制現有規範 111
第三節、管制建議 114
參考文獻 118
中文文獻

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王正嘉(2019),〈AI與人臉辨識技術運動於犯罪偵防之問題分析〉,法務部司法官學院(編),《刑事政策與犯罪研究論文集(22)》,頁235-253,法務部司法官學院。

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英文文獻

法律條文及法案
Algorithmic Accountability Act of 2019, H.R.223, 116th Congress (2019).
An Act Relating to Governmental Structures Protecting the Public Health, Safety and Welfare, VT S124 (2020).
An Act to Increase Privacy and Security by Regulating the Use of Facial Surveillance Systems by Departments, Public Employees and Public Officials, ME HB 1585 (2021).
Charter of Fundamental Rights of the European Union: 2010 O.J. (C83) 389. Proclaimed by the Commission, 7 December 2000. Proclamation and text at 2000 O.J. (C364) 1
Concerning the Use of Facial Recognition Services, WA SB6280 (2020).
Directive (EU) 2016/680 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data by competent authorities for the purposes of the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties, and on the free movement of such data, and repealing Council Framework Decision 2008/977/JHA, OJ L 119, 04.05.2016
Ethical Use of Facial Recognition Act, S.3284, 116th Congress (2020).
Facial Recognition Technology, Use of Match as the Sole Basis of Probable Cause or Arrest, Prohibited, AL SB56 (2022)
Facial Recognition Technology; Authorized Uses, VA SB741 (2022).
Fourth Amendment Is Not For Sale Act, S.1265, 117th Congress (2021).
General Data Protection Regulation, Regulation (EU) 2016/679, OJ L 119, 04.05.2016.
Governmental Use of Facial Recognition, UT SB34 (2021).
Guidelines 05/2022 on the Use of Facial Recognition Technology in the Area of Law Enforcement, Eur. Data Prot. Bd. (May 2022).
Law Enforcement: Facial Recognition and Other Biometric Surveillance, CA AB-1215 (2019).
MA GL Chapter 6 Sec.220.
No Facial Recognition at the IRS Act, S.3668 (2022)
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Vermont Statute Title 20, Chapter 205. 20 V.S.A. § 4622.

國外判決及法律案件
Brady v. Maryland, 373 U.S. 83 (1963)
Class Action Complaint, Calderon v. Clearview AI, Inc., No. 1:20-cv-01296 (S.D.N.Y. 2020)
Class Action Complaint, Hall v. Clearview AI, Inc., No. 20-cv-00846 (N.D. Ill. 2020)
Class Action Complaint, Mutnick v. Clearview AI, Inc., No. 1:20-cv-00512 (N.D. Ill. 2020)
Complaint, State of Vermont v. Clearview AI, Inc. (2020 Vt. Sup. Ct. Civ. Div. Chittenden Unit)
Katz v. United States, 389 U.S. 347 (1967).
Nardone v. United States, 302 U.S. 379 (1937).
Olmstead v. United States, 277 U.S. 438 (1928).
State v. Loomis, 881 N.W. 2d 749, 760 (2016)
State v. Skaff, 152 Wis.2d 48, 53, 447 NW2d 84 (CtApp 1989).
Townsend v. Burke, 334 U.S. 736 (1948)

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