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AI in tweets: June 2020
This month we cover the important topic of bias in machine learning. Our selection of tweets includes links to articles, podcasts and tutorials relating to AI ethics.
An important debate on bias in machine learning started after the release of an image that illustrates it:
An image of @BarackObama getting upsampled into a white guy is floating around because it illustrates racial bias in #MachineLearning. Just in case you think it isn't real, it is, I got the code working locally. Here is me, and here is @AOC. pic.twitter.com/kvL3pwwWe1
— ??Robert Osazuwa Ness?? (@osazuwa) June 20, 2020
The following tweets provide links to some important articles to read, podcasts to listen to, and tutorials to watch.
1) The CVPR tutorial en Ethics in Computer vision by Timnit Gebru and Emily Denton:
i think what we need to do, if we haven't already, is dedicate some time to watching @timnitGebru and @cephaloponderer @CVPR tutorial. and pass it on. #DataEthics #AIEthicshttps://t.co/cOwhZb2MeW
— Ted Pedersen (@SeeTedTalk) June 27, 2020
2) “Combatting Anti-Blackness in the AI community” by Devin Guillory:
Combatting Anti-Blackness in the AI Community:
In this work, we aim to elucidate the scale and scope of anti-Black bias in the AI community and provide concrete steps to mitigate this bias and build a more just community.https://t.co/Hw1hEgYnq4
— Devin Guillory (@databoydg) June 23, 2020
3) Coded Bias, the film that shows the origins of the Algorithmic Justice League:
For all who have been asking, you can finally watch #CodedBias – the film that shows the origins of @AJLUnited and chronicles my journey alongside other pioneering women fighting for algorithmic justice @black_in_ai @Data4BlackLives @datasociety @AINowInstitute #HotDocs2020 https://t.co/tjXJg5vT7S
— Joy Buolamwini (@jovialjoy) June 4, 2020
4) Three articles on how Machine Learning systems can be poorly calibrated and amplify bias:
The problem is much more beyond data bias. ML systems can be poorly calibrated (https://t.co/a49XNesonL) and bias in the data might be amplified (https://t.co/7GhKXEd3yh, https://t.co/4gA26mNbMS) i.e., simply diversifying the data source might not solve the problem. https://t.co/SOQRjmPgVo pic.twitter.com/L0ckBOh78k
— Kai-Wei Chang (@kaiwei_chang) June 21, 2020
5) Podcasts by the radical AI Podcast:
IBM, Microsoft, and Amazon have publically made statements distancing themselves from facial recognition technology. What do you need to know? In this #breakingnews episode we spoke with the incredible @rajiinio of @AINowInstitute @AJLUnited https://t.co/NSKQusOKOU
— The Radical AI Podcast (@RadicalAIPod) June 24, 2020
How do we respond to systemic racism? How do we engage with diversity and representation without reducing efforts to branding and lip service? We welcome Dr Timnit Gebru @timnitGebru to the show and are so grateful for her witness and wisdom in these timeshttps://t.co/Bqd6jfRlJE
— The Radical AI Podcast (@RadicalAIPod) June 3, 2020
This month, there have also been researchers who supported the Black in AI community:
I got 21 emails from people who reached out to someone on the list and thus will donate $21k to @black_in_ai , with more donations from @JeffDean and @cazencott .
To those who signed up: thank you, I hope you'll also be there for longer-term efforts led by @black_in_ai . https://t.co/X4Vh3UnKG1
— Nicolas Le Roux (@le_roux_nicolas) June 9, 2020
A petition signed by more than 700 researchers prevented the publication of a pseudo-science article in Springer Nature:
Springer Nature plans to publish an article "A Deep Neural Network Model to Predict Criminality Using Image Processing" that revives long discredited physiognomist pseudoscience.
Sign this petition to urge @SpringerNature to refrain from publishing. RT! https://t.co/1NP8wmLCjI
— Abeba Birhane (@Abebab) June 21, 2020
And finally, we share with you this poignant article on the discomfort felt by the data scientist Deb Raji when analyzing the COVID19 death counts:
I wrote "The Discomfort of Death Counts" for Cell's data science journal @Patterns_CP, for their series on COVID-19 & data.
I thought I would be analyzing figures – but I didn’t. Instead, it's about seeing data as humans so we can properly mourn them. ?https://t.co/uxbfJlTWBU
— Deb Raji (@rajiinio) June 23, 2020
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