SSCI一区AI&Law |郭旨龙 Policing based on automatic facial recognition

Original SIAL 法大网络与智能法研究会 2023-01-13

Artificial Intelligence and Law（《人工智能与法律》）发表中国政法大学郭旨龙老师的论文Policing based on automatic facial recognition（《论警用自动人脸识别》）。该刊为SSCI数据库收录于法学类一区（最新影响因子排名：19/154)。该刊同时为SCI数据库收录。论文共47页，22000余单词。该文以英国警方部署的自动人脸识别大规模试验引发的诉讼为线索，依据功能蠕变（function creep）的技术、社会与法律相融合视角，同时借鉴刑法上的重罪、轻罪划分，对基于自动人脸识别的治安警务的法治文明问题进行了研究。

在写作和修改过程中，作者有幸得到英国大律师Lewis Kennedy博士的及时合作，英国谢菲尔德大学法学院讲师陈家宏博士、日本筑波大学信息原则与设计系副教授于海涛博士、中国西湖大学工学院助理教授原发杰博士的倾心帮助。论文图表绘制得到了陈恒星的多次帮助。

原文致谢了北京师范大学汪庆华教授、西南政法大学梁坤教授、华中科技大学李雅男博士、吉林大学谢登科教授、中国政法大学李莉副教授的会议邀请。作者在会上报告论文相关内容并得到宝贵反馈。

1.“数字政府建设与权力监督创新”理论与实践研讨会暨第四届清华—法大“数据治理”论坛，主题发言《警用人脸识别的治理与监督》（北京2021/7/6）

2.吉林大学司法数据应用研究中心、吉林大学法学院“电子诉讼的中国模式与理论回应”研讨会，主题发言《警用人脸识别中的数据隐私法问题》（长春2021/6/26）

3.华中科技大学、湖北省高级人民法院“2021人工智能与司法大数据”国际研讨会，主题发言《自动人脸识别治安警务的法治文明》（武汉2021/5/15）

4.西南政法大学刑事侦查学院、国家安全学院“个人信息保护与数据安全立法”暨“数字时代的侦查法治”学术沙龙，报告《个人信息保护法中的警察权限》（重庆2020/11/8）

5.北京师范大学法学院数字经济与法律研究中心“个人信息保护立法的理想与现实——以立法《草案》为中心”学术研讨会，报告《个人信息保护法中的警察视角》（腾讯会议2020/11/1）

需要注意的是，不同的法域很可能有不同的法律历史和警务文化。正如日本学者星周一郎针对警务大数据与信息技术侦查的研究：“……在意识到上述缺点的同时，不应该一味强调大数据警务的消极方面，而是要在讨论其积极方面的基础上，找寻到随时变化的社会可容许性的最大公约数，进而探讨对其加以落实的平衡点，同时关注日后技术的发展与社会状况的变化，并在此基础上不断展开讨论。”

Cite this article: Guo, Z.*, Kennedy, L. Policing based on automatic facial recognition. Artif Intell Law (2022). https://doi.org/10.1007/s10506-022-09330-x（或点击左下角“阅读原文”）

Abstract

Advances in technology have transformed and expanded the ways in which policing is run. One new manifestation is the mass acquisition and processing of private facial images via automatic facial recognition by the police: what we conceptualise as AFR-based policing. However, there is still a lack of clarity on the manner and extent to which this largely-unregulated technology is used by law enforcement agencies and on its impact on fundamental rights. Social understanding and involvement are still insufficient in the context of AFR technologies, which in turn affects social trust in and legitimacy and effectiveness of intelligent governance. This article delineates the function creep of this new concept, identifying the individual and collective harms it engenders. A technological, contextual perspective of the function creep of AFR in policing will evidence the comprehensive creep of training datasets and learning algorithms, which have by-passed an ignorant public. We thus argue individual harms to dignity, privacy and autonomy, combine to constitute a form of cultural harm, impacting directly on individuals and society as a whole. While recognising the limitations of what the law can achieve, we conclude by considering options for redress and the creation of an enhanced regulatory and oversight framework model, or Code of Conduct, as a means of encouraging cultural change from prevailing police indifference to enforcing respect for the human rights violations potentially engaged. The imperative will be to strengthen the top-level design and technical support of AFR policing, imbuing it with the values implicit in the rule of law, democratisation and scientisation – to enhance public confidence and trust in AFR social governance, and to promote civilised social governance in AFR policing.

Keywords

automatic facial recognition · training dataset · learning algorithm · function creep · policing by consent · data privacy trust

1 Introduction

2 AFR-based policing: system explanation and research method

2.1 Steps and developments of AFR in general

2.1.1 A brief review of AFR process

2.1.2 Traditional and modern AFR methods

2.2 AI-based shifts for AFR in policing

2.3 Research method from function creep

3 Harms of AFR-based policing

3.1 Harms to individuals: violations of dignity, privacy and autonomy

3.1.1 Infringing dignity