"Like bad data detectives, we accepted the first clue at face value."
In last week’s email newsletter I mentioned Naomi Wolf’s data problems (That’s Naomi Wolf - not Naomi Klein as I accidentally wrote in part of the text. Apologies to Naomi Klein fans!) I wrote that if Naomi Wolf had built a data biography for your work, she could have avoided this problem. A really insightful colleague, Carolina Roe-Raymond from Rutgers University (Rutgers is also where Naomi Klein is the Gloria Steinem Chair in Media, Culture, and Feminist Studies, so I initially thought it might be another correction of my typo) wrote back that she didn’t think that our data biography template would have actually prevented this problem. We spent a lot of time talking and thinking about this during the week in our office and we realized Dr. Roe-Raymond was correct. And, with many thanks to Carolina, we’ve updated the Data Biography Template.
One really effective way to embed an equity lens into your data products is to ensure that the humans working on your project represent a diversity of world views There was an excellent panel at the annual Women in Statistics and Data Science called Preparing for Increased Gender Diversity and Inclusion in Statistics and Data Science: Important Perspectives from Gender Non‐Conforming and LGBTQ Scholars. There's a nice new paperas a result which links to another important resource on adding equity to your data with improved demographic data collection. This article on LGBT data collection amid social and demographic shifts in the United States.
Data equity heading into the weekend.
From Wired Magazine, "This Dating App Exposes the Monstrous Bias of Algorithms" Ben Berman designed a game to help us really understand what filter bubbles mean in data apps. He thinks that losing the bubble and adding some gamification would make data more fun and less racist.
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.
