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“Under Sampled Majority”
We’re almost halfway through the semester (I think) and we’ve been deliberately taking our time. Please DM me if you need any support or feel lost or just want to say hello. I’m here for you and want this asynchronous space to still feel human.
I want to jump back into thinking critically about the fields inside engineering. This goes for everyone, but especially for Computer Science majors — have you considered the ways in which your field has bias? the ways your field has a profound impact on how society is shaped?
I’m not sure if these questions are being raised in your Grove courses (I hope they are! Tell me if they are!) and since we’re considering both rhetoric and composition, these questions must be taken into account.
For this week, I would like you to watch this 13 minute talk by Dr. Joy Buolamwini about facial recognition and the effects when the sample set skews white and male.
For the module comment, I would like you to consider the following:
Take note of 2-3 rhetorical issues Dr. Buolamwini raises that speak to you. For me, it was her reframing of the “under-sampled majority” as a way to think about who is represented in most technological spaces and who is erased. So often we say “minority” when speaking about the people of the global majority who are not white and that set standard creates an intentional bias which has real implications (think policing, thinking community funding, think incarceration rates)
Have you ever considered algorithmic bias when using your devices?
What are some ways we can shift the dominant data set?
If you have an experience of algorithmic bias that you want to share, I welcome it in this space but it is not required. Or if you want to add your experience to Dr. Buolamwini, I think that would be fantastic.
Thanks everyone for staying engaged and enjoy the rest of your week!