We need data on public institutions so that we can keep them accountable. But when that data is collected and shared by the institution itself, they are in a powerful position to control the narrative. What can we as data practitioners do to help identify potentially biased narratives that could exist in this type of data? Because blindly trusting a biased dataset is just as harmful as blindly trusting a biased source. We would like to talk a little about a vetting process, and about a few important types of biases in data that you should have in mind. This list is by no means complete and is still a work in progress, but it includes some of the more major types of bias that we’ve noticed in data, and what we think you can do about them.