For anyone not familiar with the fairness literature, as a start, I recommend the classic “Fairness through Awareness” paper on how to make use of the protected class attributes to ensure fairness. https://t.co/2xZXFAZq5T https://t.co/aiYpWPg06Y
— Delip Rao (@deliprao) November 20, 2019
There are 2 big cultural failures here:
— Angela Bassa (@AngeBassa) November 19, 2019
1) folks feeling comfortable with insufficient context: “It’s not my job to understand why this needs to get done”
2) folks not feeling safe enough to gather context: “I’m too scared to ask questions” https://t.co/bOsMz7hOhD
This is such an important and underappreciated point. Simple, rule-based approaches can be good for consistency, fairness, & transparency. https://t.co/6ReoUNs3Pn
— Rachel Thomas (@math_rachel) November 19, 2019
Much of what’s being sold as "AI" today is snake oil. It does not and cannot work. In a talk at MIT yesterday, I described why this happening, how we can recognize flawed AI claims, and push back. Here are my annotated slides: https://t.co/iCpyFw5urN pic.twitter.com/pmOTI3vq8p
— Arvind Narayanan (@random_walker) November 19, 2019
Personally identifiable information (PII) is the new plutonium:
— Brandon Rohrer (@_brohrer_) November 19, 2019
* difficult and expensive to store safely
* powerful if painstakingly refined
* valuable on the black market
* can destroy lives accidentally if mishandled
* can destroy societies if weaponized https://t.co/i0wMU6yE2S
5 Lenses for Ethics:
— Rachel Thomas (@math_rachel) November 16, 2019
- utilitarianism
- rights
- common good
- fairness or justice
- virtue@IEthics #TechPolicyCADEhttps://t.co/3HKgZuW6NN pic.twitter.com/GItdLbUIj1
We don't deserve machine learning.
— hardmaru (@hardmaru) November 15, 2019
“Provide a tool that can gauge a person's personality just from an image of their face. This can then be used by an HR office to help out with sorting job applicants.” 🤨🤔 https://t.co/RbjV7qLWVU
So many AI hiring systems are appallingly bad.
— Janelle Shane (@JanelleCShane) November 14, 2019
Better to draw the resumes at random out of a hat; at least then you know it won't be copying bias. https://t.co/0FKY69l7Jx
I was interviewed by @Slate’s Aaron Mak about the Apple Card discrimination problem: https://t.co/OFs0RDv4pB
— Cathy O'Neil (@mathbabedotorg) November 12, 2019
This is an exceptionally important article about @google and health care data being provided by @Ascensionorg, without patient or doctor knowledge. So do we, as a society care – what protections are there? @ChrisMurphyCT @SenBlumenthal @rosadelauro https://t.co/TN4yuuI632 @WSJ pic.twitter.com/jaWcjUBv2M
— Harlan Krumholz (@hmkyale) November 12, 2019
A few pieces of advice for Apple:
— Cathy O'Neil (@mathbabedotorg) November 11, 2019
1. don't deny this algorithm is a problem, and don't pretend it isn't your problem.
2. figure out what people actually want to know; the answer isn't your valuable source code, so please don't claim you cannot address it by hiding behind IP. https://t.co/RjpO4VX86a
There's been some distributing ML applications on my TL recently: ETS's automated essay grading w/ demonstrable bias against African American students & the paper attempting to automate criminal sentencing (ht @there_are_two_) for example.
— Rachael Tatman (@rctatman) November 6, 2019
Here're my thoughts (as a flow chart). pic.twitter.com/ffzzPNbi5g
