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If the idea of tech not being neutral is new to you, or if you think of tech as just a tool (that is equally likely to be used for good or bad), I want to share some resources & examples in this thread. Please feel free to suggest/add additional resources! 1/
A classic paper exploring this is "Do Artifacts Have Politics?" by Langdon Winner (written in 1980), on whether machines/structures/systems can be judged for how they embody specific types of power 2/

cc.gatech.edu/~beki/cs4001/W…
Surveillance technologies are aligned with power. The panopticon is not going to be used to hold the warden accountable: 3/

Paper by @farbandish on how algorithms to externally assign/label a person’s gender are inherently anti-trans: 4/

Joe Redmon, one of the creators of the YOLO architecture for object detection, later stopped doing computer vision research because of the implications: 5/

Great talk from @radical_ai_ asking us to consider how our machine learning models shift power, given at @QueerinAI #NeurIPS2019 6/

slideslive.com/38923453/the-v… Black screen. In crossed out letters: Is this ML model doin
It is a pattern throughout history that surveillance is used against those considered "less than", against the poor man, the person of color, the immigrant, the heretic. It is used to try to stop marginalized people from achieving power @alvarombedoya 7/

Papers by @morganklauss on how computers classify/categorize identity, race, & gender, and some of the implications & issues around this: /8

A curriculum by @BlakeleyHPayne for middle-schoolers to explore how technology is not neutral & to start redesigning it:

Some people mistakenly think that more diverse datasets can solve all our problems. When I teach @harini824's framework of types of bias, I particularly emphasize *historical bias*, which can't be addressed with a different dataset or model: /10

Yes, 💯 recommend the works in this thread:
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Rachel Thomas
@math_rachel
This is how science actually works. It’s less the parade of decisive blockbuster discoveries that the press often portrays, and more a slow, erratic stumble toward ever less uncertainty. Our understanding oscillates at first, but converges on an answer. 1/ theatlantic.com/health/archive… This is how science actually works. It's less the parade of decisive blockbuster discoveries that the press often portrays, and more a slow, erratic stumble toward ever less uncertainty.
There’s a lot of expertise around, but fewer tools than ever to distinguish it from everything else. Pure credentialism doesn’t always work. People have self-published terrible pieces on Medium, but some of the best early explanations for laypeople were from tech guys. @zeynep 2/ We hunger for information, but lack the know-how to evaluate it or the sources that provide it.
The issue is that modern expertise tends to be deep, but narrow. But pandemics demand both depth and breadth of expertise. No one knows it all, and those who claim to should not be trusted. @edyong209 3/ The issue is that modern expertise tends to be deep, but narrow. Even within epidemiology, someone who studies infectious diseases knows more about epidemics than, say, someone who studies nutrition. But pandemics demand both depth and breadth of expertise. To work out if widespread testing is crucial for controlling the pandemic, listen to public health experts; to work out if widespread testing is possible, listen
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Rachel Thomas
@math_rachel
Some folks are uncritically arguing that only *architectures* matter in deep learning, not datasets nor anything else (So Fei-Fei Li creating ImageNet didn't contribute much 🙄)

This narrow tunnel focus is so harmful to the field.
Other things that matter in deep learning (and that we should start valuing more): framing problems, collecting data, interpreting results, communicating results, encouraging adoption, ETHICS of what we're doing

This is a great paper by @timnitGebru @unsojo on why we should be giving a lot more thought to how we curate our datasets in ML:

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Rachel Thomas
@math_rachel
I’m watching the same arguments about masks over & over & over again, so I wanted to share some common misconceptions/objections about masks, and their rebuttals. Many thanks to @zeynep & @jeremyphoward for the fantastic work they’ve done in making the case for masks. 1/
First, if you haven’t read this literature review from 19 interdisciplinary experts on the research on masks, please do so 2/:

But ordinary people wear masks incorrectly! Many people also wash their hands incorrectly, but we haven’t given up on that 3/:

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