What is visualization for? Is it a tool help us understand data and the world, and to make better decisions because of that? Or is it just a debugging tool, a stepping stone towards intelligent machines?
As machines are getting smarter and more capable, we expect them to make more decisions for us. While that is natural, it’s easy to walk into a trap: to think that we can hand things over to machines entirely and stop caring and understanding. But things don’t work like that.
This is not a new idea. In their enthusiasm about early successes in the 1960s, computer scientists thought that they would soon be able to build thinking machines, electronic brains, that could perform many human tasks as well as, if not better than, us. And today, we’re no closer to building a thinking machine than we were 50 years ago. Watch this fantastic documentation from 1992, The Machine That Changed The World, take a critical look. In particular, the guy talking at 18:33 about how translators will soon be out of a job.
I like to think of visualization as putting the human back into the decision-making process. Rather than trusting algorithms to figure things out, I want to see the data and make the decision myself. It’s not just the decision itself, it’s about knowing why things are done. Understanding the world around us is one of our most fundamental human urges, and one of the things that set us apart from animals and machines.
Michael Driscoll says that Visualization is a Halfway House. What he means is a halfway house on the way to fully automated systems. And look how terrible things are with visualization!
But data visualizations still require human analysts to react and kick off another action, if they are to be useful.
Tim O’Reilly picks up the story (which is based on a comment he made) and describes visualization as debugging and exception-handling:
There will be many areas where visualization interfaces enable exception handling. But more and more, expect services that used to require you to look at a screen and make a decision to just make that decision for you.
Sure, some decisions can be made better by a computer. But a surprising number of them require a lot more thinking than you’d expect. A machine might be able to make a decision, but is it a good one? Also, why did it make that decision? Have you ever wondered why Amazon recommended some nonsensical product to you that you had no use for? Why would you trust a machine to make a decision for you that’s actually important?
I also take issue with the comparison with maps and the assertion that they don’t serve a purpose beyond going from A to B (which your GPS unit and, eventually, your self-driving car will do much more efficiently). There are studies that show that kids that are driven everywhere instead of riding their bikes or walking lose their sense of direction and distance. People who only drive by GPS direction don’t know the areas they drive in, even if they do so regularly, and can’t reliably give directions.
Machines that make our lives easier are a good thing. Machines that move heavy objects, that let us move faster, that keep our houses at the right temperature. Good machines, useful machines.
But when it comes to thinking, machines aren’t nearly as helpful. Thinking is not a chore, and understanding the world is not a necessary evil. Opaque decisions made by machines that don’t explain remove us from the facts and make us stupid. If we lose our ability to navigate, we are no longer in control of the space. These machines make us lazy, complacent, and dependent. Bad machines.
If visualization is a halfway house, it’s a halfway house to a dystopia. And I’d rather stop halfway, where things work, where I can understand them, and where I give a damn. Giving up our ability to understand the world means giving up our humanity. And a little bit of convenience cannot possibly make up for that.
As an aside, if you haven’t seen The Machine That Changed The World, you owe it to yourself to watch it. It’s not just a fantastic introduction to the history of computing and many of its key players, it’s also a wonderful time capsule in itself (it came out in 1992). All five parts are on YouTube: part 1, part 2, part 3, part 4, part 5.
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