The Sigma Mobile Advertising Blog

Sorry for the off-topic discussion, but I think would be useful to shed a little light on why it’s not as easy to automate the detection of potential terrorists as many people think. Think of this as a combination of demographic, behavioral, and contextual targeting. If your favorite ad platform doesn’t give you a 100% click-through rate, don’t be surprised if the government’s counter-terrorism platform doesn’t either.

I worked on similar problems in the first half of my career so I think I can add a little to the discussion. Comments to this post are welcome but please focus on the computational problem, not the political problem.

First, a little background.  The intelligence community has been trying to automate this sort of thing for decades. Some really, really smart people are involved. If you think it would help to “just throw some Stanford, MIT, and Carnegie Mellon Ph.D.s at the problem”, let me assure you, I’ve been to conferences on this subject that were filled with Stanford, MIT, and Carnegie Mellon Ph.D.s.

Automated intelligence can be organized at two levels: information fusion and indications & warning.

Information Fusion

Data fusion and information fusion are processes for collecting and merging data from multiple sources into coherent “pictures” of a situation. Think of de-duping mailing lists; i.e., recognizing that John Smith, J. Q. Smith, John Q. Smith, Johnny Smith, John Smiht, etc., are the same person. Ever looked at your credit report and seen items you don’t recognize? It’s probably from someone with a similar name and there wasn’t enough information for the credit bureau to determine exactly who it belonged to.

Now remember that the data received by credit bureaus is orders of magnitude more accurate than what can be gotten from overseas and particularly third-world sources. In many places it’s easy to change the name on your idenity card or passport — I see people leave Hong Kong and reenter under slightly different names all the time.

Indications & warning

Indications & warning (commonly termed I&W) is the high level process of making sense of fused data. You can think of it as finding a high-level model that fits low-level data, or asking the question, “What’s the meaning of what we’re seeing?” In general, it’s a recognition problem with many analogs such as disease diagnosis, manufacturing fault diagnosis, image understanding, or even natural language understanding (“what’s the meaning behind this collection of characters?”).

How would you go about building an I & W system for counter-terrorism? The obvious approach is to build a model of how to recognize a terrorist and fit data to the model. So, how do you build a model of a potential terrorist? It’s not easy. From what I’ve read, there aren’t any single defining characteristics. How about combining multiple characteristics, e.g., “received warning from father” + “traveled to Yemen”? Now you need to combine and weigh evidence. The simple way to do this in expert systems is by assigning weights, summing them, and applying a threshold. A better way is Bayesian probability, which considers conditional probabilities. But, how do you get those probabilities, and how do you know what’s significant and what’s not? Finally, how do you set a threshold? Make it too high and screen out terrorists while making innocent people unhappy; make it too low and people end up dead.

A better approach might be anomaly detection: instead of trying to model what terrorist are like, model what normal traveler are like and then look for anomalies. This might catch the terrorist who travels internationally on a one-way ticket without luggage. So, terrorists with half a brain (or more likely the people managing them) will do a better job in disguising themselves.

Conclusion

It’s common for smart people to underestimate the difficulty of problems especially in hindsight. This cognitive error is known as hindsight bias. According to Wikipedia:

Hindsight bias is the inclination to see events that have occurred as more predictable than they in fact were before they took place. Hindsight bias has been demonstrated experimentally in a variety of settings, including politics, games and medicine. In psychological experiments of hindsight bias, subjects also tend to remember their predictions of future events as having been stronger than they actually were, in those cases where those predictions turn out correct.

Sound familiar?

Here’s a possible solution that focuses on immediate behavior. Performing behavioral analysis at the airport makes sense but it requires TSA staff with a much higher level of skill than what I’ve seen and it also doesn’t help when dealing with flights coming from other countries.