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If we're talking about the specific example - DeepAI's API isn't perfect (as the twitter thread demonstrates), that's about it. > Am I reading too much into th

by jstx1 5y ago

If we're talking about the specific example - DeepAI's API isn't perfect (as the twitter thread demonstrates), that's about it. > Am I reading too much into this Kind of. If you're trying to generalize to all ML problems based on this one example, the discussion is too broad to say anything meaningful. Try to apply your questions to the specific example and you'll see how little sense they make: - consulting experts - who would be the expert when trying to guess the color of a 19th century carpet? - faith in the results - does anyone care about the specific results in this case? I mean it's a cool API but the end result is purely aesthetic, it's not like something important is happening in the world based on the results - do ML practitioners have responsibility to communicate to the general public - no, the people who are writing the ML API have a responsibility to their customers and to the internal product stakeholders in the company. The general public has nothing to do with any of this. And then there's hundreds different configurations of [ML application + person creating it + company they work for + user + real world impact] for which the answers will be completely different.

SleekEagle 5y ago
Thanks for the reply! I don't really have a good frame of reference for the model-improvement feedback loop in different industries, so I could very well be generalizing incorrectly. As for the experts part, I assume there is some expert out there who would know enough about the economy/trade at the time that they could glean enough information about materials, dyes, etc. to make an educated guess/ Faith in results - while maybe in this simple example that's valid, what about in cases where ethics are concerned? IIRC there was a police force using an ML model to predict where units should be patrolling and it turned out they were overpolicing primarily black neighborhoods. Same thing with ML systems for employee applicant screening. Communication - I think that this is a fair point, although given how pervasive ML is at this point I think it's good to try make sure the view of the public is not too skewed (like the whole "God particle" fiasco several years ago) Either way, thanks for the reply! You make some good points and I don't think many of my questions were well-framed enough.

by jstx1 5y ago

SleekEagle 5y ago

Thanks for the reply! I don't really have a good frame of reference for the model-improvement feedback loop in different industries, so I could very well be generalizing incorrectly. As for the experts part, I assume there is some expert out there who would know enough about the economy/trade at the time that they could glean enough information about materials, dyes, etc. to make an educated guess/ Faith in results - while maybe in this simple example that's valid, what about in cases where ethics are concerned? IIRC there was a police force using an ML model to predict where units should be patrolling and it turned out they were overpolicing primarily black neighborhoods. Same thing with ML systems for employee applicant screening. Communication - I think that this is a fair point, although given how pervasive ML is at this point I think it's good to try make sure the view of the public is not too skewed (like the whole "God particle" fiasco several years ago) Either way, thanks for the reply! You make some good points and I don't think many of my questions were well-framed enough.