AI’s Potential as a Natural Disaster Preparedness Tool
In an ASIS research project on security trends published earlier this year, 15 percent of security professionals reported that they used artificial intelligence (AI) to analyze threat capabilities or probabilities. It was one of eight use cases asked about in the survey.
Other use cases included “added surveillance object detection or motion tracking capability” and “added advanced biometrics or facial recognition to access control.” Of the eight, more security professionals said they wanted to learn more about the use of AI to analyze threat capabilities and probabilities (65 percent) than any other use case. (For more on the study, see the ASIS research report Understanding the Evolving Role of Security, which was sponsored by Resolver.)
In another ASIS research project on threat intelligence and organizational resilience, 72 percent of security professionals said obtaining good intelligence on natural disasters was extremely or highly important for an effective response, but only 58 percent said their organization was extremely or highly effective at gathering natural disaster intelligence. (See Threat Intelligence: Understanding How Threat Management Supports Resilient Organizations, sponsored by Esri, for more.)
Could security and risk intelligence teams use AI to fill that gap?
“AI can be used to prepare for disasters before they happen, and respond once they occur,” wrote Patrick S. Roberts, senior political scientist for RAND in a commentary last week about AI’s potential in helping to mitigate natural disaster damage as well as aid recovery. “Machine learning models can process vast datasets and forecast fires, floods, and hurricanes with greater precision than traditional methods.”
To understand the potential of AI in such cases, Roberts created a table outlining how different AI tools could enhance disaster preparedness, identification, and response. He also pointed out the risk that reliance on AI could lead to decisions that are not necessarily aligned with “human values, goals, and intentions.”
|
Tools |
Description |
Examples of Commercial Systems |
Uses in Emergency and Disaster Management |
|
Predictive analytics |
Finds patterns in data and forecasts future outcomes |
Salesforce |
Risk modeling; disease outbreak spread prediction; flood/wildfire spread prediction; dashboards and situational awareness |
|
Generative AI and natural language processing |
Understands and translates human language and creates new text, images, or video |
ChatGPT, Claude, DALL·E |
Drafting emergency communication templates; creating scenarios for training; multilingual crisis communication; rumor detection |
|
Robotics and automation |
Performs physical tasks with or without human control, including operating vehicles |
iRobot Roomba; Da Vinci Surgical System; Boston Dynamics robots; Waymo |
Search-and-rescue in dangerous areas; supply delivery; debris clearing |
|
Computer vision |
Identifies and interprets objects, people, and activities images/video |
Google Photos; Clearview AI; Tesla Autopilot |
Damage assessment via drones/satellites; search-and-rescue; wildfire smoke mapping |
|
Speech recognition and generation |
Converts speech to text and produces human-like speech from text |
Siri, Alexa |
Voice-to-text for field reporting; hands-free operations |
|
Recommendation systems |
Suggests products, content, or actions based on user behavior |
Netflix, Spotify, Amazon |
Resource allocation; shelter options; individual risk alerts |
|
Fraud Detection & Security |
Identifies anomalies to call attention to risks |
Mastercard AI Security, Darktrace, PayPal |
Detecting fraud in payments; cybersecurity |
Table is reproduced with permission of RAND from “How AI Is Changing Our Approach to Disasters.”
“The use of AI to manage disasters is in its early days, but the table shows its potential for a range of uses,” Roberts wrote.
The commentary also has a bulleted list of ways that organizations have used AI to mitigate potential negative effects of natural disasters.
“Like any tool or outsourced activity, using AI well will require setting up expectations and legal and technical guardrails, and working with stakeholders to make sure the AI does what we want,” Roberts wrote. “The private sector is making big investments in the technology, but potential users also need to invest in understanding and planning for how best to use it.”
