TY - GEN
T1 - A call for more explainable AI in law enforcement
AU - Matulionyte, Rita
AU - Hanif, Ambreen
PY - 2021
Y1 - 2021
N2 - The use of AI in law enforcement raises several significant ethical and legal concerns. One of them is AI explain- ability principle, which is mentioned in numerous national and international AI ethical guidelines. This paper firstly analyses what AI explainability principle could mean with relation to AI use in law enforcement, namely, to whom, why and how the explanation about the functioning of AI and its outcomes needs to be provided. Secondly, it explores some legal obstacles in ensuring the desired explainability of AI technologies, namely, the trade secret protection that often applies to AI modules and prevents access to proprietary elements of the algorithm. Finally, the paper outlines and discusses three ways to mitigate this conflict between the AI explainability principle and trade secret protection. It encourages law enforcement authorities to be more proactive in ensuring that Face Recognition Technology (FRT) outputs are explainable to different stakeholder groups, especially those directly affected.
AB - The use of AI in law enforcement raises several significant ethical and legal concerns. One of them is AI explain- ability principle, which is mentioned in numerous national and international AI ethical guidelines. This paper firstly analyses what AI explainability principle could mean with relation to AI use in law enforcement, namely, to whom, why and how the explanation about the functioning of AI and its outcomes needs to be provided. Secondly, it explores some legal obstacles in ensuring the desired explainability of AI technologies, namely, the trade secret protection that often applies to AI modules and prevents access to proprietary elements of the algorithm. Finally, the paper outlines and discusses three ways to mitigate this conflict between the AI explainability principle and trade secret protection. It encourages law enforcement authorities to be more proactive in ensuring that Face Recognition Technology (FRT) outputs are explainable to different stakeholder groups, especially those directly affected.
KW - AI
KW - Explainability
KW - Law enforcement
UR - http://www.scopus.com/inward/record.url?scp=85122982220&partnerID=8YFLogxK
U2 - 10.1109/EDOCW52865.2021.00035
DO - 10.1109/EDOCW52865.2021.00035
M3 - Conference proceeding contribution
AN - SCOPUS:85122982220
T3 - IEEE International Enterprise Distributed Object Computing Conference Workshops-EDOCW
SP - 75
EP - 80
BT - Proceedings - 2021 IEEE 25th International Enterprise Distributed Object Computing Conference Workshops, EDOCW 2021
PB - Institute of Electrical and Electronics Engineers (IEEE)
CY - Piscataway, USA
T2 - 25th IEEE International Enterprise Distributed Object Computing Conference Workshops, EDOCW 2021
Y2 - 25 October 2021 through 29 October 2021
ER -