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How can I add an equity lens to my data if I've already collected the data?
 

There are seven stages to the data lifecycle. And it's great when you can embed attention to equity in each stage. However, we all live in the real world, where sometimes we're playing catchup. There are ways to include indicators in your data models that will help increase the equity of your results even after your data has been collected.

We've put together a video with an example of how to do this. You can watch it here.

A quick example of how modeling can increase equity and accuracy in your data projects.
If you're interested in meeting in real life we'll be speaking on ethics and equity in data at the Go Open Data (GOOD) Conference in Toronto next week. We'll be covering data biographies. Check out Catherine D'Ignazio's new piece on putting data in context for her most recent insights and tips. This article is aimed at journalists but is great for everyone.
Tech Review did a great set of interviews with a variety of experts on what practical steps can be taken to build equity and ethics into Artificial Intelligence (AI). The impetus for the piece was the fiasco around the Google AI panel, which makes for useful insight into how corporations are struggling to deal with these issues.

We're looking for equity problems and successes.

If you have a story or idea you want to share, send me a note by replying to this email.

Project for Equity in Data Science
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