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RAND Corporation - Systemic Risk Assessment
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4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: RAND Corporation
A RAND Corporation policy research report relevant to AI governance and systemic risk; useful for those studying how AI failures could cascade across societal systems and what regulatory or international coordination frameworks might mitigate such risks.
Metadata
Importance: 52/100organizational reportanalysis
Summary
This RAND Corporation report examines systemic risks posed by advanced AI systems, analyzing how failures or misuse could cascade across interconnected critical systems. It provides a structured framework for understanding risk pathways and governance interventions at national and international levels. The report aims to inform policymakers on proactive risk mitigation strategies.
Key Points
- •Identifies how AI-related failures can propagate across critical infrastructure, financial systems, and governance structures through cascade effects.
- •Applies systems-thinking methodology to map interdependencies and vulnerabilities that amplify AI-related risks.
- •Examines governance mechanisms at national and international levels to contain or prevent systemic AI risk.
- •Highlights the role of compute governance as a lever for managing the development and deployment of high-risk AI systems.
- •Provides risk pathway analysis useful for policymakers designing regulatory frameworks for advanced AI.
Cited by 2 pages
| Page | Type | Quality |
|---|---|---|
| AI Risk Cascade Pathways Model | Analysis | 67.0 |
| AI Governance and Policy | Crux | 66.0 |
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Using Video Analytics and Sensor Fusion in Law Enforcement: Building a Research Agenda That Includes Business Cases, Privacy and Civil Rights Protections, and Needs for Innovation | RAND
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The diffusion of video technology means that law enforcement will increasingly have streaming video feeds from in-car and body-worn cameras that may be monitored to help protect the safety of officers and bystanders. This report examines the potential of such technologies while weighing the challenges they pose, the innovation that they need to be ideally developed and implemented, and the civil liberties protections required by their use.
Using Video Analytics and Sensor Fusion in Law Enforcement
Building a Research Agenda That Includes Business Cases, Privacy and Civil Rights Protections, and Needs for Innovation
John S. Hollywood , Michael J. D. Vermeer , Dulani Woods , Sean E. Goodison , Brian A. Jackson
Research Published Dec 28, 2018
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The diffusion of video technology means that law enforcement will increasingly have streaming video feeds from in-car and body-worn cameras that may be monitored to help protect the safety of officers and bystanders. The proliferation of internet-enabled digital video cameras and sensor devices (part of the Internet of Things) provides public safety agencies with a huge technological opportunity. The new but emerging fields of video analytics and sensor fusion offer potential for addressing these challenges. On July 12–13, 2017, on behalf of the National Institute of Justice and the Priority Criminal Justice Needs Initiative, the RAND Corporation, assisted by the Police Executive Research Forum, held a workshop examining these issues. The workshop participants constructed business cases for the use of these tools in law enforcement, identified key innovation needs with respect to their application, explored the types of behavior and objects such tools would be designed to detect, and identified key civil rights and civil liberties protections required for their use. The results were brought together into a research roadmap for this topical area regarding application of these technologies in policing.
Key Findings
Video analytics and sensor fusion are extremely promising technologies for improving public safety
There are 22 high-priority needs for innovation to enhance the effectiveness and security of video analytics and sensor fusion (VA/SF) for law enforcement.
VA/SF could be of great benefit in detecting crimes in progress and investigating crimes and incidents.
VA/SF could support law enforcement by monitoring officer performance and protecting
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