Reading Annotation Blog 3.3

 

In forecasting who would re-offend, the algorithm made mistakes with black and white defendants at roughly the same rate but in very different ways. The formula was particularly likely to falsely flag black defendants as future criminals, wrongly labeling them this way at almost twice the rate as white defendants. White defendants were mislabeled as low risk more often than black defendants. 10
Benjamin, Ruha. Race after Technology : Abolitionist Tools for the New Jim Code, Polity Press, 2019. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/wsu/detail.action?docID=5820427.
Created from wsu on 2023-04-08 03:25:54.

Reading thought the newly assigned chapters of Race after Technology, a lot more issues with how some blatantly discriminatory designs and technology still exists. I like how this part of the chapter ties into what we talked and learnt about in the last unit, which is in regards to algorithms. From personal experience of using the internet I know sometimes big companies like to use the word "glitch" as an excuse when question on why their algorithms are seemingly discriminatory; For example recently there was some controversy over how some creators of certain races were having their videos recommended less, limiting their exposure and thus, their livelihoods. As someone who spends a lot of their time on the internet, I hope such practices will not be common moving forwards, and that the current ones would be abolished immediately. 

To further touch upon this topic, I find the idea of a "Default Discrimination" to be absolutely abhorrent. The predictions and recommendations of this algorithm that everyone uses is a serious problem if it already has discriminatory practices built into it already, and I'm surprised that nobody has fixed it yet.