Some folks have asked about data vs. algorithms. Treating these as separate silos doesn't really make sense, and it contributes to a common perception that the data is someone else's problem, an unglamorous & lesser task:
Machine learning often fails to critique the origin, motivation, platform, or potential impact of the data we use, and this is a problem that we in ML need to address.
Q: Is AI development trapped in a paradigm that pursues efficiency above all else? @ResistanceAI
@Abebab cites ongoing work that finds efficiency, accuracy, & performance are the key values mentioned in most ML papers
Q: Is AI development trapped in a paradigm that pursues efficiency above all else?
@red_abebe: Efficient for whom? With example of criminal justice system, is it efficient to have 2 million in USA in prison?
Noopur Raval: The efficiency paradigm can show up in unexpected forms, including many projects claiming to be for social good. Technology can appear part of a mystical, deceptive promise to make things better.
This idea that you can't highlight problems without offering a solution is pervasive, harmful, and false.
Efforts to accurately identify, analyze, & understand risks & harms are valuable. And most difficult problems are not going to be solved in a single paper.
I strongly believe that in order to solve a problem, you have to diagnose it, and that we’re still in the diagnosis phase of this... Trying to make clear what the downsides are, and diagnosing them accurately so that they can be solvable is hard work -- @JuliaAngwin
With industrialization, we had 30 yrs of child labor & terrible working conditions. It took a lot of journalist muckraking & advocacy to diagnose the problem & have some understanding of what it was, and then the activism to get laws changed
I know about diversity-washing, I know about the empty lip-service. But I still can't get past the contrast between @JeffDean's tweets (h/t @EricaJoy) and his treatment of @timnitGebru-- never having a conversation with her, not telling her manager, denying her DEI experience,...
I have long admired @timnitGebru for her brilliance, moral courage, clear voice in speaking up for what is right, & influential scholarship. It is truly terrible that Google would do this.
In this thread, I want to share some of Timnit's work I love
I've quoted "Datasheets for Datasets" (2018) in many of my talks & assign it as reading in my class. It highlights decisions that go into creating & maintaining datasets, and how standardization & regulation came to other industries
Timnit worked with @jovialjoy on the original GenderShades research, which has had a profound impact on facial recognition, led to concrete regulations, and changed our industry
Reciprocity is a key part of life. Surveillance undermines reciprocity. Every time we opt for surveillance or extractive technology, we undermine reciprocity and relationship. -- @doxtdatorb#AgainstSurveillance
Between 1971-1974, a Detroit Police Department surveillance unit called STRESS (Stop The Robberies, Enjoy Safe Streets) fatally shot 24 people, 22 of them African-American @hypervisible#AgainstSurveillance
A freelance journalist in Vietnam w/ 150,000 followers & a verified Facebook account realized all his posts about a high-profile death penalty case had vanished with no notification