#technological bias

[ssmhhh]

— The Amber Spyglass (2000), Ch. 14 “Know What It Is”

⁎⁺˳✧༚

the following excerpt was the very first piece of theory assigned to me as a communications undergraduate and it always stuck with me as particularly resonant. i could never pinpoint exactly as to why that was:

— Neil Postman, Technopoly (1993), Ch. 1 “The Judgment of Thamus”

（´-`）.｡oO

…five years later I revisited the HDM trilogy on a whim, not fully recollecting the reasons for which i had buried much of the story deep in the backyard of my subconscious mind 12 years prior.

ʕ⁎̯͡⁎ʔ༄

as soon as i arrived at this passage in the amber spyglass it all made so much more sense to me, both the complex thematic weight of the chapter as well as Postman’s theoretical concepts that I so memorably grasped at in my clumsy initial foray into academia.

((((；ﾟДﾟ)))))))

turns out my memory blackout was an act of self-preservation reflexive to the trauma of my devastation over the ending, which, now that i am older, wiser, and more educated can see that it was full of holes and basically pullman’s failure to imagine a world where capitalism and labour is less valuable than love, interpersonal bonds, and human connection (i have a whole other post about that.)

ಠ_ಠ … ( ´Д`)y━･~~

[axlotl9]

A Century of “Shrill”: How Bias in Technology Has Hurt Women’s Voices

Technological bias is about more than audio quality—it’s about the forces that influence whose stories are told and how.

Summary: microphones and radio transmissions were optimized for male voices, meaning women using microphones do end up sounding “shrill” absent proper equalization.

[archivistic]

Algorithms, he writes, “take a complex system from the world and abstract it into the processes that capture some of that system's logic and discard others.” This is important to remember, however, because our view of algorithms as objective processes is dependent [on] the absence of human interference. But as Andreas Ekström (2015) reminds us in his TED talk, The Moral Bias Behind your Search Results, “behind every algorithm is always a person, a person with a set of personal beliefs that no code can ever completely eradicate.” We might add that behind every algorithm is also a company, with obligations to its business model and shareholders.

Matthew Reidsma, Algorithmic Bias in Library Discovery Systems