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Resource ID: #1174
Subject: 318
Source: 2702Anna Flagg, Moiz Syed
Affiliation: The Marshall Project; The Intercept
Date: 2017



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.

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