So this is crazy fcuked.
Despite their denial, Gizmodo caught the College Board (who administer the SAT, etc.) sharing scores and GPAs with Facebook, TikTok, etc.
https://gizmodo.com/sat-college-board-tells-facebook-tiktok-your-scores-gpa-1850768077
So this is crazy fcuked.
Despite their denial, Gizmodo caught the College Board (who administer the SAT, etc.) sharing scores and GPAs with Facebook, TikTok, etc.
https://gizmodo.com/sat-college-board-tells-facebook-tiktok-your-scores-gpa-1850768077
@pluralistic "neo-Brandeis" is new to me!
"The New Brandeis or neo-Brandeis movement is an antitrust academic and political movement in the United States which argues that excessively centralized private power is dangerous for economical, political and social reasons."
Let’s de-enshitify the internet. I just pledged to donate to @pluralistic’s Kickstarter for his upcoming book “The Internet Con: How to Seize the Means of Computation.” Wonderful thread below 👇🏻
https://pluralistic.net/2023/07/31/seize-the-means-of-computation/
Today's threads (a thread)
Inside: Kickstarting a book to end enshittification, because Amazon will not carry it; and more!
Archived at: https://pluralistic.net/2023/07/31/seize-the-means-of-computation/
1/
"Algorithms are to bias what centrifuges are to radioactive ore: a way to turn minute amounts of bias into pluripotent, indestructible toxic waste."
Brilliant quote from @pluralistic in https://pluralistic.net/2023/07/26/dictators-dilemma/.
Training a machine learning algorithm is a very conservative act: it starts from the training data -- the past -- and presumes that's precisely what you want in the future. So it's conservative in several senses: conserving past observations/data; making the future just like the past, and so on.
If that training data is good, and conserving past behavior is really and truly what you want, this is great!
But, wait: what does "good" mean? And who decides what we really and truly "want"?
Required reading, in my not-at-all humble opinion, is "Data Feminism": https://data-feminism.mitpress.mit.edu. My biggest criticism of their book is the title, which can mislead you into thinking it's somehow specific to gender-related issues, but it addresses bias of all kinds, and how we, as a society, use algorithms.