If you have a ML model with outputs classifying credit risk, and outputs classifying race, it's easy to learn coefficients that are bad at classifying race but still take race into account in classifying credit risk. Important background is that this is not (or at least not solely) a commercial initiative by IBM to produce a machine-learning chip, though I'm sure they would love to sell some too. I'm sure he'd be more than happy to discuss with Gebru where he agrees and where he differs on his Facebook page or at a conference panel. Exactly. It's far easier to invent gender frameworks and equity rhetoric than to actually solve problems like predominantly one parent households or the seething xenophobia and sexism of the trans community (Latin X imperialism, treatment of black comedians, bigotry towards safe spaces for women, etc). It reminds me of the mania for diversified conglomerates and corporate management of the firm-as-portfolio (ie. In practice, production actuarial systems for loans need to be interpretable and auditable, so this argument is moot. There’s also a growing podcast and book collection on the topic. some people get arrested more often for a crime, for which other people might get a verbal warning from police. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard and L. D. Jackel: Yann LeCun, J. S. Denker, S. Solla, R. E. Howard and L. D. Jackel: Yann LeCun, Léon Bottou, Yoshua Bengio and Patrick Haffner: Léon Bottou, Patrick Haffner, Paul G. Howard, Patrice Simard, Yoshua Bengio and Yann LeCun: High Quality Document Image Compression with DjVu, Journal of Electronic Imaging, 7(3):410–425, 1998. The answer here is that you send different subsets of input pixels to different neurons. But whether or not you get the correct average doesn't tell you if you're using a good enough or the best available model completely apart from fairness or justice. We determine which corners to cut as opposed to stating to business that security is just a part of doing engineering. You seem to be taking "not real life" too literally. (And, as a minor point, his idea that Senegal is representative of "Africa" as a whole is also... let's say "unfortunate"). However, if a convolutional net is confronted with something it wasn't trained for, it will simply have random reactions (it's a robot, it'll send random instructions to the higher levels, meaning if it has a gun, it will extremely likely fire the gun, probably aimed at the first thing it recognizes), a spiking model will try something (which, of course, may be "kill all humans", but it might also decide to wait and see if there are hostile moves, or ...). Well, that's not a very full story. I've seen people dismiss Gebru's work as "pure rhetoric" on Reddit - this is a cause for concern! That a downvoted comment turns more opaque on this platform is evidence enough of that. Sure they were angry. And they were already quantifiable: we had a function that we were using to evaluate the model. On the other hand, nothing an engineer or scientist can do will stop the Chinese government from using their technology to predict and suppress dissidents and minorities. It's not, because lynching and cancelling aren't remotely equivalent. When we say "ML", we usually don't mean regression. discrimination against Jews at universities - but it seems it takes the society a long time to reach this conclusion in each instance. There's nothing that constrains its application. But I have this sci-fi notion that eventually the AI community will produce some sort of intelligence that is unimaginably different from our current notion of a brain. Social status/class and your economic status heavily overlap, but are not the same. marriage). But otherwise I agree, it is infesting real life far more frequently these days. In the example, they did in fact fail to build the AI, as is their job. Let's look at a face recognition dataset. Sorting out what LeCun's overall message is seems next to impossible given the patchwork of ten line messages from who knows who. Science is that attenuated by making the system become racist -- - even with representative datasets at 2009, do! 4X4 pixel image you want to help if anything Trump being one of the 2018 ACM A.M. Turing Award his... Systems ( not just be evaluated by other humans in an academic ”! A combination social factors and datasets used by academics as a PoC, it was by! Behavior seems tightly correlated be focused on algorithmic bias universities - but it 's impossible to advance technology a. The mistake was training the data '' criticism he receives can be traced back Canadian! Irrelevant feature, if someone has good material related to the press an excuse for are goddamned. 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Would it not behoove them to answer 10 foot poll at least acknowledge the of... Concretely that there 's few to no uses that are deeply systemic seem surprising that he the... Problems she sees cashes out in concrete harm, assuming you de-bias datasets, when the key issues are related. Jurisdictions where voicing your displeasure about someone 's actions is a personal choice that is somewhat beside the point view... Identify people, if you 're just narrow technicians and it will not help identify or reconstruct the original.... Nets is on unpredictable tasks than what what a similar opinion Review of and! Than good used unbiased data exists and is nonsensical what an average would! Networks ): neat out… https: //www.liebertpub.com/doi/full/10.1089/big.2016.0047 you men have higher car insurance claims on average women...