Abstract
Techniques described herein define a law-enforcement framework for multi-agent artificial intelligence (AI) systems that enables lawful, proportionate, and auditable access to digital intelligence generated within distributed AI environments. The framework introduces a structured separation of responsibilities across multiple cooperating agents, ensuring that legal authorization, data observation, AI-based analysis, intelligence correlation, and oversight are logically and operationally decoupled. No single agent is permitted to authorize, collect, analyze, and report intelligence, thereby reducing abuse risk and single points of failure.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
M M, Niranjan and Bailkeri, Medini Narasimha, "LAW ENFORCEMENT FRAMEWORK FOR MULTI-AGENT AI SYSTEMS", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9998