Big data is everything these days. There is absolutely no question that there is more information out there than ever before and more ways to collect it. Everyone from companies to police forces are getting into the game with a view to earning more money or making their jobs easier and more efficient.
Nor is there any doubt that having more data is better than having less. This is especially true when it comes to terrorism studies. I have long complained that much of the literature to be found is highly theoretical and shows little evidence of being data driven or, where there is some data, it is seldom robust enough to support the claims made. Of course in a field like terrorism, where those with the bulk of the information are law enforcement and security intelligence agencies, it is not surprising that data is generally unavailable.
This shortcoming has done little to stop well-intentioned individuals from devising models and programmes to describe, explain and, in some cases claim to predict, violent extremism. Over the course of my time at CSIS I saw many of these efforts and while I found most to have interesting and useful elements, none were perfect and none could have been applied to a real, constantly-changing operational environment. In fairness, the most honest providers of these models would admit that their products were tools and not infallible instruments.
There is of course a great danger in seeing any model as Gospel. Models are only as good as their inputs and the data used to construct them. If small data samples preclude certain phenomena, the model cannot handle them. And if that same data stream is heavily weighed in a biased direction it will also fail. This is the problem known as false positives and false negatives.
Despite these gaps, models keep getting pushed forward as indispensable tools for our law enforcement and security intelligence officials. Here is an example. Recently, Correctional Services Canada has been struggling with the very controversial issue of segregation. The government, and the public, want to see the numbers of inmates kept in isolation lowered and the pressure is mounting on CSC. For its part, the union representing prison staff has raised the danger to their members should the use of segregation diminish. And in what is even scarier, and more germane to this blog, the union fears that what was once a decision made by humans has now been relegated to a computer questionnaire that determines whether an inmate should be isolated.
In other words, we are allowing a machine with algorithms to decide who poses the greatest threat to staff and other prisoners. I have no idea what those algorithms are or what data (and how much) was used to create them, but am I the only one who sees this as mad? A field that requires human input is now left to a programme.
I fear that this rush to automate everything – from manufacturing to driving to medical diagnosis – will end badly. At a minimum, human livelihoods in the form of jobs are at stake while at a maximum actions will be taken (or not taken) based on models that may not actually be accurate.
Perhaps it is time to recognise that some things, like why people become terrorists, are unknowable in any useful generalised fashion. Perhaps it is time to acknowledge that for some things “it depends” is as good as it gets.