151–160 of 683 posts
If the authors aren't confident enough in their paper to release it publicly, why would I want to read someone else's (presumably inferior) summary of it?
"...is known for coauthoring a groundbreaking paper that showed facial recognition to be less accurate at identifying women and people of color, which means its use can end up discriminating against them" Wait, isn't that the other way around? If it can't recognize people of some category then it can't be used to discriminate against them, e.g. the police can't use it to identify peaceful protesters with those charac…
If face detection defines them as not a person, then things that rely on there being a person in the field of vision will not work for them. (Like the racist soap dispenser that went viral a few years ago) If face recognition makes the old racist "they all look the same to me" declaration, then peaceful protestors get arrested for looking like criminals.
“There are two ways that this technology can hurt people,” says Raji who worked with Buolamwini and Gebru on Gender Shades. “One way is by not working: by virtue of having higher error rates for people of color, it puts them at greater risk. The second situation is when it does work—where you have the perfect facial recognition system, but it’s easily weaponized against communities to harass them. It’s a separate and connected conversation.” [0]
[0] https://www.technologyreview.com/2020/06/12/1003482/amazon-s...
Ok, so basically she is a bullshit social engineer, masquerading as an 'AI ethics researcher' "Gebru’s draft paper points out that the sheer resources required to build and sustain such large AI models means they tend to benefit wealthy organizations, while climate change hits marginalized communities hardest. “It is past time for researchers to prioritize energy efficiency and cost to reduce negative environmental i…
They didn't say they shouldn't make them, they said "prioritize energy efficiency".
normal language, that people use
As they said, language changes. Do we want a model that acts like the average person of the last 20 years? Or do we want a model that acts like a person who has just been through 2020?
Despite what both sides claim, IMHO papers like this are primarily PR, ideology, and politics, rather than science and technology. This is akin to a speech writer working for a politician, writing a piece that disagrees with the party platform, and refusing to fix it when asked.
She didn’t “refuse to fix it when asked”. She agreed with the proviso that she could have a meeting to figure out what she was and was not permitted to publish. The response was to decline to meet and fire her. It’s entirely plausible that the paper is bunk; however, when someone is willing to come down that hard to prevent an idea being published, I tend to err on the side of “worth finding out what”.
Ok, so basically she is a bullshit social engineer, masquerading as an 'AI ethics researcher' "Gebru’s draft paper points out that the sheer resources required to build and sustain such large AI models means they tend to benefit wealthy organizations, while climate change hits marginalized communities hardest. “It is past time for researchers to prioritize energy efficiency and cost to reduce negative environmental i…
Driving to the library, that is the alternative to training these models, really?
> -- Ok, basically she is pissed as AI is picking normal language, that people use, and not using the vocabulary that is in vogue on certain political circles. Basically censure, and forced speech.
If you had a model trained on a large corpus of data from the pre civil war southern American states, it would have been deeply racist, and would even view black people as possible property. If you had one that was trained on data from the 1950 it would be less racist but still problematic viewed by people from today. Is there really something special with today, that removes these kind of concerns with a model trained with current data?
> I think social engineering b.s. should be kept as far away as possible from science. This is turning true ai research into a masquerade to push certain political agendas.
It seems to me that it would be impossible to do any social science research, and specifically any research on racism. With this kind of attitude.
Some of the concerns brought up in the paper seems less consequential than others. Especially the pollution one seems weak to me, that doesn't make it false, and fair enough that is was brought up. I find the racism issue a lot more problematic, and is something I've run up to working on deep learning solutions myself. Even if it worked fine for my group, and most of our customers, that is definitely not fun, and something practitioners should consider when building these things.
Ok, so basically she is a bullshit social engineer, masquerading as an 'AI ethics researcher' "Gebru’s draft paper points out that the sheer resources required to build and sustain such large AI models means they tend to benefit wealthy organizations, while climate change hits marginalized communities hardest. “It is past time for researchers to prioritize energy efficiency and cost to reduce negative environmental i…
> Has she calculated the oposite alternatives? People driving to libraries, to search for something? What has this got to do with anything? How is driving to a library the "oposite" of AI? We had very effective search engines for decades before AI.
Ok, so basically she is a bullshit social engineer, masquerading as an 'AI ethics researcher' "Gebru’s draft paper points out that the sheer resources required to build and sustain such large AI models means they tend to benefit wealthy organizations, while climate change hits marginalized communities hardest. “It is past time for researchers to prioritize energy efficiency and cost to reduce negative environmental i…
I think you’re making a weaker version of this argument than you need to, because you’re framing it in terms that oppose the politics of half of the public, when you really only need to argue against a much more specific set of ideas. For example: > Ok, basically she is pissed as AI is picking normal language, that people use, and not using the vocabulary that is in vogue on certain political circles. Basically censu…
Exactly this. Framing it in terms of alternate options makes AI the more reasonable choice.
Google AI appears to have drawn unnecessary attention to themselves for relatively benign paper. Most of the arguments in this paper have already been discussed elsewhere and could be refuted in simple terms.
The paper DID NOT force her out of Google. Her subsequent behaviour - submitting without approval, rant, ultimatum, and resignation - did. And she wasn't "forced out": she resigned of her own volition. She could have chosen to make improvements to the paper based on the feedback she was given, resubmit it for approval, and then get on with her life, but she went the other way.
The headline from the last discussion on Timnit's exit[0] was awful as well: "The withering email that got an ethical AI researcher fired at Google". So bad in fact that it was changed on HN to more accurately reflect what actually happened: "AI researcher Timnit Gebru resigns from Google" (much more neutral and factual in tone).
Seriously, what happened to journalistic standards and integrity? Why are the actual events being so forcefully twisted to fit a particular narrative? No wonder the general population struggle to figure out what's true and what's not, and fall victim to all kinds of nonsense, BS theories, and conspiracies.
I wish I had a good idea on how to change this behaviour by journalists and publications.
(Clearly this is a problem that goes far beyond Timnit's story.)
See the other thread were one mentions that with reCaptcha Google learns that all cabs are yellow (and traffic lights hang over streets - they don't here). But I guess everyone only sees the blind spots of other people.
You're assuming that reCaptcha is training some Google algorithm. But in fact it has trained you -- to recognise US style dangly traffic lights, yellow school busses and cabs, pedestrian cross walks -- and this makes me wonder if reCaptcha does use the same image set for all different locations. Are people in Lagos, Pune and Tashkent routinely failing reCaptcha at higher rates because they don't watch US television?…
Basically I know the US better - or the image it projects - than my own country.
Funnily it was a huge letdown when I visited for the first time decades ago, as it felt just like a sitcom and there was nothing new really.
Ok, so basically she is a bullshit social engineer, masquerading as an 'AI ethics researcher' "Gebru’s draft paper points out that the sheer resources required to build and sustain such large AI models means they tend to benefit wealthy organizations, while climate change hits marginalized communities hardest. “It is past time for researchers to prioritize energy efficiency and cost to reduce negative environmental i…
>-- Has she calculated the oposite alternatives? People driving to libraries, to search for something? Driving to the library, that is the alternative to training these models, really? > -- Ok, basically she is pissed as AI is picking normal language, that people use, and not using the vocabulary that is in vogue on certain political circles. Basically censure, and forced speech. If you had a model trained on a large…
I think this argument applies not just to machine learning, but to learning in general. Any kind of knowledge-acquisition process is going to be biased by the environment in which it occurs. That goes not just for digital neural networks, but also those in our human brains, operating on the same racist data the ML models are. If that means we shouldn’t do machine learning, it also means we shouldn’t do human learning either.
Of course, the preceding is absurd. A more reasonable take is that we should adjust the objective function of our learning processes to try to account for the effects of biases. We try to do that subjectively as any decent person operating in a biased society should, but our ML models can do it more accurately. In fact, I’d argue that such techniques are necessary to more carefully analyze and build evidence describing the effects of those biases. They can provide insights that will even improve our ability to correct for biases in the real world.