In today’s age, we all know how data is part of everything we do. By just going through our day to day activities, we create information about who we are, and how we move through the world. This data can, and has, been used for amazing things. From small things like presenting you content that you’d enjoy, to supporting doctors with complicated diagnoses. However, as with all great things, they can be misused; willfully, or by ignorance and laziness. That’s why it’s important to be aware, to be active in how we see data. When data is seen as the holy grail, the pure source from which all information can be gained, we overlook the context of that data. Using our data, we must evaluate the inherent biases. Who may be missing from the data? What discrimination may be baked into your information? What underlying issues are we not seeing, and therefore perpetuating?These questions aren’t easy, but it’s our responsibility to tackle them as best we can, and make sure we avoid doing harm.
Originally studying engineering physics at Chalmers, my interests drifted mainly towards programming and in particular machine learning. Today, I’m working in the Analysis team at Recorded Future, where we attempt to give context and structure to the vast textual data available online. My focus lies on how to get and maintain quality data for our models and analysis.