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<p>The imperative to meticulously assess and manage the legal and ethical risks associated with artificial intelligence (AI) in public safety management is increasingly recognized. This study employs the Apriori algorithm to identify frequent itemsets in public safety risk management, further refining these findings using the FP-growth algorithm’s Gini coefficient to pinpoint optimal features representing legal-ethical risk factors. Cloud modeling techniques are also applied to examine the nuances of AI’s legal ethics. Our analysis reveals a significant growth in AI patent applications within the public safety sector, showing an increase in the relative growth rate from 1.1679 to 1.4810 over eight years, equating to an 88.66% rise. Based on highest membership values in the risk prevention and control system, risk categorization identified social ethics risk and public security threat risk with indices of 0.461, 0.721, and 0.499, respectively, classifying them into class II and III risks. This investigation into AI’s legal ethics forms a critical foundation for developing a risk regulation framework and offers strategic recommendations for legal reform, ensuring AI’s positive trajectory in public safety.</p>
</abstract>

The imperative to meticulously assess and manage the legal and ethical risks associated with artificial intelligence (AI) in public safety management is increasingly recognized. This study employs the Apriori algorithm to identify frequent itemsets in public safety risk management, further refining these findings using the FP-growth algorithm’s Gini coefficient to pinpoint optimal features representing legal-ethical risk factors. Cloud modeling techniques are also applied to examine the nuances of AI’s legal ethics. Our analysis reveals a significant growth in AI patent applications within the public safety sector, showing an increase in the relative growth rate from 1.1679 to 1.4810 over eight years, equating to an 88.66% rise. Based on highest membership values in the risk prevention and control system, risk categorization identified social ethics risk and public security threat risk with indices of 0.461, 0.721, and 0.499, respectively, classifying them into class II and III risks. This investigation into AI’s legal ethics forms a critical foundation for developing a risk regulation framework and offers strategic recommendations for legal reform, ensuring AI’s positive trajectory in public safety.

A Legal and Ethical Review of Artificial Intelligence Technology in Public Safety Management

Law School of People’s Public Security University of China

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The imperative to meticulously assess and manage the legal and ethical risks associated with artificial intelligence (AI) in public safety management is...

A Legal and Ethical Review of Artificial Intelligence Technology in Public Safety Management

Data publikacji: 03 maj 2024

Zakres stron: -

Otrzymano: 11 kwi 2024

Przyjęty: 26 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-0954

Słowa kluczowe
Apriori algorithm, FP-growth algorithm, Gini coefficient, Cloud model, Artificial intelligence

© 2024 Wanyu Ning, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Legal and Ethical Review of Artificial Intelligence Technology in Public Safety Management

Data publikacji: 03 maj 2024

Zakres stron: -

Otrzymano: 11 kwi 2024

Przyjęty: 26 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-0954

Słowa kluczoweApriori algorithm, FP-growth algorithm, Gini coefficient, Cloud model, Artificial intelligence

© 2024 Wanyu Ning, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Apriori algorithm, FP-growth algorithm, Gini coefficient, Cloud model, Artificial intelligence