I'm an activist about childhood poverty and education. This makes its way into my data science work because I pursue projects and analyses that allow me to understand how poverty affects education progress. I often explicitly state my biases, because I am sure my interpretation of data is colored by my pre-existing beliefs. Yet, I often discover analyses that I wouldn't otherwise have considered due to my beliefs — and I have integrity — I continually update my beliefs based on data. So, on one hand I agree that being an activist can bias one's interpretation of data, but I also think that the biases can be essential for generating useful hypotheses to guide analysis.