@60Minutes producers spoke to me for many hours. I even spent additional time building a custom demo for @andersoncooper and made recommendations on research to include and subjects to interview with emphasis on the stories of the #excoded who have been falsely arrested. 1/n
There was an interview scheduled and on my way to receive a COVID19 test I got a last minute notification that the interview had been canceled before I even reached the destination. It recalls to mind how @reshmasaujani and @GirlsWhoCode were handled by @60Minutes. 2/n
I was glad to see the work of @ClareAngelyn highlighted as it rightfully should be. From a public education standpoint I am not clear why @60Minutes did not include any of the results from the Dec 2019 @NIST study. nist.gov/news-events/ne…
@AJLUnited we will continue to do the work and also speak out against the continual erasure of our voices, our research, and our lives.
Shoutout to @shalinikantayya for her tireless work directing the documentary film @CodedBias which centers the lives and voices of the excoded and people negatively impacted by #facialrecogntion. Shout out to @naacpimageaward for using your platform to celebrate the marginalized.
So much wasted time fighting erasure. For all we have done via @AJLUnited, I still wonder where we could be with full energy. #CodedBias#VoicingErasure
• • •
Missing some Tweet in this thread? You can try to
force a refresh
venturebeat.com/2020/08/23/the…
This analysis of the Gender Shades paper seems to miss how it has been employed to shift policy + industry practices contribute to legislative wins against harmful tech, & expose the lie of technical superiority of AI powered systems and their makers.1/n
Gender Shades and the following Actionable Auditing paper were positioned in a manner that opened up space for computer scientists and AI researchers to actively oppose the police use of facial recognition technology. medium.com/@bu64dcjrytwit… 2/n
GS and AA papers authored by young black women challenged norms of evaluation in computer vision, ML, & biometric tech by exemplifying why intersectionality cannot be ignored. The take away is not "optimize oppressive systems" but to rethink the technologies evaluated 3/n