Great list of resources on the syllabus for Ethics in NLP course @emilymbender @UW https://t.co/gg9aZBWvDN pic.twitter.com/6QnU1myCAJ
— Rachel Thomas (@math_rachel) June 10, 2019
Great list of resources on the syllabus for Ethics in NLP course @emilymbender @UW https://t.co/gg9aZBWvDN pic.twitter.com/6QnU1myCAJ
— Rachel Thomas (@math_rachel) June 10, 2019
How many of the people on Google’s short lived ethics board were on the payroll of Alphabet or otherwise we’re indirectly getting money from Google?https://t.co/hihBjLIXBq
— Cathy O'Neil (@mathbabedotorg) June 8, 2019
Ethics is being used by big tech as a smokescreen to delay responsibility and avoid liability. It’s not pegged to anything. It’s self-regulation by another name. - @mer__edith
— Matt Cagle (@Matt_Cagle) June 8, 2019
🙏🏼Preeeeach.🙏🏼#CityArtsLectures pic.twitter.com/lrKJuLx8aR
This doesn't surprise me. Algorithms match jobseekers to jobs in ways that propagate past practices. STEM jobs will go to men, daycare jobs to women, and we'll see tons of automated ageism. https://t.co/2yMG0BOEqL
— Cathy O'Neil (@mathbabedotorg) June 7, 2019
Some slides I threw together for an "Explainable AI" meetup last night. Let's call the talk about some combination of data ethics, explainability, and ML engineering best practices.
— Joel Grus ♥️ 📓 (@joelgrus) June 7, 2019
"You have 34 slides!"
"That's right"
"It's a 10 minute talk!"
"👍"https://t.co/kP13NNqCwa
Algorithms aren’t some pure math form that falls ready-formed from above in etched stone tablets. Mathematical applications are subject to bias!
— Angela Bassa (@AngeBassa) June 5, 2019
For instance, 3/5 is just math. Counting someone as three fifths of a person IS NOT. https://t.co/iLxasZyXZc
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned Tutorial https://t.co/57N8JdKhZ1@mmitchell_ai @emrek @benhutchinson @kkenthapadi @packer_ben
— Rachel Thomas (@math_rachel) June 5, 2019
(deleted previous tweet bc I forgot the link)
I wrote about the failure of YouTube execs to take their recommendation system issues seriously: https://t.co/EpSo82ac2M
— Rachel Thomas (@math_rachel) June 3, 2019
Any user who watched one kiddie video would be directed by YouTube's algorithm to dozens more — each selected out of millions of otherwise-obscure home movies by an incredibly sophisticated piece of software that YouTube calls an artificial intelligence. The families had no idea.
— Max Fisher (@Max_Fisher) June 3, 2019
YouTube’s algorithm has been curating home movies of unwitting families into a catalog of semi-nude kids, we found.
— Max Fisher (@Max_Fisher) June 3, 2019
YT often plays the videos after users watch softcore porn, building an audience of millions for what experts call child sexual exploitationhttps://t.co/zNwsd9UsgN
1. The systematic mass internment and property seizure of Uyghurs and Tibetans (and the willingness of the Chinese people to look the other way) is deeply troubling. China is not the first country to do this, nor the last. https://t.co/xim8DIjBW5
— Eric Jang (@ericjang11) June 3, 2019
Chinese researchers (and one from Australia) put together a dataset of Uyghur, Tibetan, and Korean — all ethnicities mainland is oppressing right now. #MachineLearning community should be enraged about this, but instead this journal tags it as a “focus article”. (H/t @slashML) pic.twitter.com/BAotcCVjlg
— Delip Rao (@deliprao) June 3, 2019