Timnit Gebru Is Building a Slow AI Movement - IEEE Spectrum
►https://spectrum.ieee.org/timnit-gebru-dair-ai-ethics
Timnit Gebru was a well-known scholar in the AI-ethics community long before she got fired by Google in December 2020—but that messy and dramatic incident brought a new level of attention to her work. Google apparently exiled Gebru from its AI ethics team (and subsequently fired the other leader of the team) in response to a paper about the dangers of the large language models that have become so important to the world’s biggest technology companies. The episode created a firestorm in the AI field.
But Gebru has made the most of the jarring opportunity. In December 2021, she announced the founding of a new organization, the Distributed AI Research Institute (DAIR), which is billed as “a space for independent, community-rooted AI research free from Big Tech’s pervasive influence.” Since then, Gebru has been staffing up. In February, AI and sociology researcher Alex Hanna joined as research director, departing from her Google job with a blistering resignation letter. Gebru and Hanna spoke with IEEE Spectrum about their plans for DAIR.
Alex Hanna: I’m not necessarily thinking about it from the perspective of the precautionary principle. I’m thinking of it more from the perspective of developing technology that works for people. A lot of the AI research that happens right now is AI for the value of AI itself. A lot of people are thinking about this body of tools known as AI and saying, “Well, everything looks like a nail, and we have this big hammer.”
We already know that deep learning has problems. These modes of research require organizations that can gather a lot of data, data that is often collected via ethically or legally questionable technologies, like surveilling people in nonconsensual ways. If we want to build technology that has meaningful community input, then we need to really think about what’s best. Maybe AI is not the answer for what some particular community needs.
It’s interesting to hear you talk about challenges with the data sets. Timnit, in your work on large language models you’ve called attention to problems with existing data sets, including embedded bias. The response I often hear is, essentially, “It’s just too hard to make data sets better.”
Gebru: If it’s just too hard to build a safe car, would we have cars out there? It goes back to what Alex was saying about a hammer. People think there’s just one way to do it. We’ve been trying to say, “Maybe there’s a different way we can think about this.” If you think [data-set curation] is a necessity, that means it’ll take more time and resources before you put something out there.
Hanna: This is a point we’ve made over and over. We’ve published on data-set practices and how many of these things go out with not enough attention paid to what’s in them. This data-hungry version of building models started with ImageNet, and it wasn’t until ImageNet was out for about 10 years that people started to dig in and say, “Wait, this [data set] is really problematic.”
Merci @fil pour le lien.
#Timnit_Gebru #Alex_Hanna #Intelligence_artificielle #Travail_de_la_donnée