"I have very strong opinions about programming, but one rule I try to follow is 'do not mock other programmers'.
— 👩💻 DynamicWebPaige @ $HOME 🏞 (@DynamicWebPaige) August 11, 2018
Programming is too big, and I’m too small to understand everything.
Disagreeing is fine; laying out why people are wrong is fine; making fun of them is *not* fine." pic.twitter.com/uBoiqc7rxS
Many of the most chilling stories of algorithmic bias don’t involve a meaningful appeals process, perhaps because people incorrectly assume algorithms won't make mistakes.https://t.co/zGxUlJ8xhs pic.twitter.com/5PSzi0uzkM
— Rachel Thomas (@math_rachel) August 9, 2018
So @rj_gallagher has uncovered some of the technical lengths Google went to in order to censor its own search results in China and they're fairly breathtaking. Surreptitious query monitoring, elaborate probing of the great firewall — even a censorship API. https://t.co/miCQ78adJX
— Ryan Tate (@ryantate) August 8, 2018
The utilitarianist perspective on ethics is deeply questionable -- it presupposes that we know what goal to pursue (we don't) and that we can predict the impact of choices wrt our progress towards that goal -- not only first order impact, but 2nd order and 3rd order as well.
— François Chollet (@fchollet) August 5, 2018
This is a BEAUTIFUL example of how current commercial-grade AI can fail to incorporate basic priors about the world, sometimes with deadly consequences. Thank you, @PositiveExist https://t.co/yz36235zpr
— Gary Marcus (@GaryMarcus) August 4, 2018
Key proposal by @bhecht in Nature this week:
— Berk Ustun (@berkustun) August 3, 2018
“The CS community should change its peer-review process to ensure that researchers disclose any possible negative societal consequences of their work in papers, or risk rejection.”https://t.co/ETItLEi7QE
Consider the type of world that you want to live in, and design your work to create it. The fourth piece in our data ethics series considers our responsibility for the outcomes of our work: https://t.co/Vclr1vuX7Q
— Hilary Mason (@hmason) August 2, 2018
ICYMI, the @EFF report on law enforcement use of facial recognition is pretty thorough https://t.co/lqn5pFMufY
— Rachel Thomas (@math_rachel) July 27, 2018
Want Less-Biased Decisions? Use Algorithms. Nice to see these studies taking place. https://t.co/GShe3qjDEm
— Nando de Freitas (@NandoDF) July 27, 2018
I'd love to publicly debate the author of this article. https://t.co/9x1ZTH9yz1
— Cathy O'Neil (@mathbabedotorg) July 27, 2018
This ACLU story is an example of the type of 'empirical investigation' that I think many news organizations should adopt when analyzing aspects of AI. Don't listen to us (the companies), just use our tech and test it. https://t.co/5Iix7vk3jn
— Jack Clark (@jackclarkSF) July 26, 2018
This reminds me of the predatory for-profit rehab centers using Facebook support groups to target addicts and their families: https://t.co/59k1BKE3k7
— Rachel Thomas (@math_rachel) July 26, 2018
