Doing evil with data: a beginner’s guide
Why choose evil? It is not only much more fun, you get paid a lot more.
By Tim McElligott
The concept of evil has been co-opted by spiritualists and makers of horror films to represent something otherworldly, an amoral force impressing its will from beyond. But evil is often simply a choice. It is a choice among humans deciding how they want to wield a new-found power or advantage. Big data presents such an advantage and there will be those who choose to use it for public and private benefit, and those who purposely choose to apply it in ways that harm others and benefit only themselves.
Since it is often easier to choose evil, and in the opinion of data scientists Duncan Ross and Francine Bennett, sometimes more fun, the two offered last week a beginner’s guide for using data for evil.
Why choose evil? It is not only much more fun, it gets you paid a lot more they said, as a lead into their spoof and cautionary tale on becoming an evil overlord of data without really trying.
Duncan Ross has been a data miner since the mid-1990s. He now leads the international data science team at Teradata. Francine Bennett is a data scientist and CEO and co-founder of Mastodon C, which sells an open source technology platform and skills for big data analytics.
Bennett says it doesn’t take much for data scientists to be evil. “A talented data scientist can be evil and it doesn’t have to be about big things. We all have the ability to make the world a tiny bit worse by our behaviors,” she said.
Ross says to do evil, one has to think a little bit about the role of data in the world at the moment and the view that the media takes of data. He accuses the media of having a built-in scare factor when it comes to data. “One of the easiest ways you can be evil … is simply by not doing good. You are encouraging the media to think about data and databases in terms of 1984,” he said.