Artificial Intelligence and the ‘Bomb-in-a-Box’ Scenario: Risk-Based Oversight by Disruptive Technology

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This case was submitted as part of the Call for Innovations, an annual partnership initiative between OPSI and the UAE Mohammed Bin Rashid Center for Government Innovation (MBRCGI)

Transport Canada has piloted the use of Artificial Intelligence (AI) to perform risk-based oversight by scanning Pre-loading Advance Cargo Information (PLACI) to identify potential air cargo threats (i.e. “bomb-in-a-box” scenario). By using AI to perform this function, there is potential for Transport Canada to save 1,000’s of hours and conduct risk assessments on all air cargo shipments coming from abroad -- in real-time.

Innovation Summary

Innovation Overview

To keep pace with the rapid changes in the transportation sector, Transport Canada must explore using disruptive technologies like artificial intelligence (AI). In “thinking big,” Transport Canada set its sights on AI augmenting every process and procedure within the department, freeing up humans to work on the ‘sticky’ problems. However, Transport Canada recognizes the importance of “starting small” with an ideal use-case that allows for applicable testing. Then, if that case is successful, “scale fast” to other areas of the department and possibly, the whole of the federal government.

In this context, Transport Canada identified the perfect use-case for AI with its Pre-load Air Cargo Targeting (PACT) team that receives approximately 1 million air cargo messages per year. For example, if one employee, working at an unrealistic rate of one message per minute, spent all their time reviewing messages, they wouldn’t even have enough time to review 10% of all messages received in a year.

To date, there are very few governments that have the dedicated resources to scan air cargo messages for risk before cargo is loaded and of the ones that do, none use AI. When considering the resources responsible for customs and border protection, no country is using AI in that capacity either.

This use-case represented a true collaborative effort by senior executives from different areas of the department. The PACT team resides within the Aviation Security area of the department and is being directly supported by the Digital Services (IT) area of the department. Transport Canada is investing effort to renew the way it conducts business, so strong support also comes from the Transformation area of the department. Additionally, Transport Canada hired one of Canada’s Free Agents to lead the project. To top it off, the department did not have enough AI capability in-house, so it partnered with an outside firm that subcontracted with an award-winning IT firm with expertise in AI.

Through this use-case, Transport Canada attempted to answer two questions:
1) Can we use AI to improve our ability to conduct risk-based oversight?
2) How can we improve our effectiveness and efficiency when assessing risk in air cargo shipments?

To help answer these questions, we proceeded in two steps. In the first step we used machine learning (ML) on a subset of the data and in the second step, we used natural language processing on a different subset of the data.

In the first step, we embedded a Transport Canada employee onsite with the IT firm. We took two different ML approaches in working with the subset of data: unsupervised learning and supervised learning. In unsupervised learning, we blinded ourselves to the ‘outcome’ of the risk assessment applied to the cargo message to try and learn about the relationships between all cargo messages. Then, we used supervised learning, where we tried to learn the relationship between the inputs (cargo messages) and the outcome (i.e. did this cargo message warrant a greater level of risk). In the second step, we used natural language processing on a different subset of data with the goal of being able to automatically tag a cargo message with a risk indicator based on ‘free text’ fields.

The results from steps one and two were very promising – AI will increase safety and security 15-fold, as every single message will be risk-assessed. Additionally, through better use of resources, PACT can use AI to increase capacity, while minimizing the number of people required to do the work.

PACT’s capabilities before introducing AI:
*Very burdensome to conduct data analysis
*Duplication of effort in data handling tasks (200+ hours to import, clean, and archive data)
*Single dedicated resource for scanning cargo messages through batch processing

PACT’s capabilities after introducing AI:
*Save 1,000’s of hours by allowing for timely and proactive data analysis of cargo data
*Conduct risk assessment on all cargo in real-time
*AI will support PACT in meeting its security outcomes, while allowing PACT to scan cargo messages from more air carriers

For Transport Canada to stand up a team of targeters similar to their counterparts around the world, PACT would have to hire 24-36 new employees. Not only does the AI save on all the hiring costs, it realizes the productivity of an ‘employee’ that can work 24 hours a day, 7 days a week – without needing to take a break.

Beyond the gains for PACT, this model could be adapted to aid in targeting other modes of transportation (e.g. marine, rail, road, etc.) or even expanded to support the mandate for Canada’s agency responsible for customs and the border. Ideally, all government departments with an interest in the safety and security of Canada – including intelligence, border, and police agencies – would have a single database with information that could be used to optimize the process for providing risk-based oversight to cargo. Thinking bigger, maybe even governments worldwide.

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Year: 2018
Level of government: National/Federal government

Status:

  • Implementation - making the innovation happen
  • Evaluation - understanding whether the innovative initiative has delivered what was needed

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