@katecrawford@trevorpaglen https://t.co/2OhP776Zac points a segmentation model at a set of 100 influential photographs curated by time magazine, and lets you explore what the segmentation mask sees, and what photographs are behind it
Let's goooo!!! The second of two papers on AI education is coming up in a bit. As an AI educator focused on inclusion and co-generative pedagogy, I'm *really* excited for this talk on exclusionary pedagogy. Will tweet some take-aways in this thread:
This is absolutely brilliant work! ❤️
imho it's up there with some of the best work on CS education👇
Epistemological Pluralism
@STurkle & Papert 1990
Stuck in The Shallow End
@joannagoode13 & pals 2010
You Can't Sit With Us
@rajiinio@morganklauss@amironesei 2021
💻❤️😎
🗣️Finally, it's out! It's here!
In this paper, me, @morganklauss & @amironesei present work on a topic people remain hesitant to discuss - the **power dynamic between disciplines** & how that shapes poor AI ethics education and practice.
Check it out: https://t.co/f2ByPGx5q1
so, while the technical work of algorithmic transparency is important, it should be part of a broader perspective on how these systems are engineered, produced, and used. having a nice technical model of a dog's vision system won't alone tell me if it will bite me. (6/7)
My @FAccTConference@DiverseInAI talk Fairness Metrics & Non-Discrimination Law: Can Fairness be Legally Automated? is online ft my paper Bias Preservation https://t.co/RZ7R2WVucW & Why Fairness Cannot Be Automated https://t.co/HgSgvclGkn @oiioxford@turinginst@CompSciOxford
A year or so ago while working on #datacards, I told @ZaldivarPhD "transparent documentation seems like an opaque deal. We need to make it more human." Today, we are stoked to open-source 1/2 of the #datacardsplaybook & present it at #facct2021#facct21
https://t.co/ieQgOATXcv
I need a name for that threshold when research findings about a category of people does more harm than good, when evidence becomes stereotype that hurts the exceptions. Favored research methods elide human diversity & complexity. #research#inclusion#DataScience
What are the responsibilities of researching AI in the Global South? Many common to all fieldwork: How can research be productive not extractive? Awesome #FAccT2021 tutorial "AI on the Ground" with incredible scholars @VidushiMarda@so_radhikal @tetisheri + Ranjit Singh!
Fairness research in ML remains largely rooted in Western concerns: the injustices, datasets, measurement scales, & legal tenets used. These infrastructures, values, & legal systems cannot be naively generalized to non-Western countries -- @autopoietic https://t.co/sryHFxZ1Xj
We need to upgrade our dataset culture in ML. If we ever hope to make our models work, data needs to be more than an afterthought - its the engine driving the field & we need to treat it as such!
Inspired by @timnitGebru & others who continue asking questions on ML's data use.
Continually grateful to @mmitchell_ai and @timnitGebru for creating a team that supported and fostered this kind of work. Also huge credit to them both for their seminal research in this area, which has paved the way for this work.
In related news, the call for CRAFT workshops at @FAccTConference 2021 is live! We're looking for workshops that creatively engage with and address critiques of the FAccT field. Please share far and wide.
https://t.co/ZVl0nbxA1S