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There's a lot of complaints here about "erasing reality" and other hyperbolic talk, but these models are trying to make predictions within a particular user's c

by sagarm 9y ago

There's a lot of complaints here about "erasing reality" and other hyperbolic talk, but these models are trying to make predictions within a particular user's context. It's just inappropriate to apply some global biases for a particular user, and avoiding that can result in a better user experience.

joe_the_user 9y ago
It's just inappropriate to apply some global biases for a particular user, and avoiding that can result in a better user experience. The problem is that most deep learning programs aren't intended for (and really be for) one user out there in user space. Deep learning programs are written for the large institutions which have the large troves of data need to train large nets and want to make important decisions using that data. But by this, those decisions won't be made in isolation but will effect a large number of people, people who aren't the users but rather the used. And if such systems have biases relative to the whole, it is a problem. And problem may not be for the institutions immediate goals but for the people who depend on the institution.
haberman 9y ago
I think you make an interesting point: if we use context to train a model to reflect local biases instead of global biases, will that be more just and/or lead to better user experience? It seems related to the question of whether Google results should be tailored to you. If I Google "did Russia interfere in the election", should Google tailor the results so I always see articles that reinforce my world view? If we go that route, I think we take the path of Stephen Colbert's concept of "Truthiness", where we judge something as true because it "feels" true. Users will definitely be happier if everything they see reinforces their existing world view. So companies will be incentivized to accommodate this desire. But does this actually lead to a more just society?

by sagarm 9y ago

joe_the_user 9y ago

It's just inappropriate to apply some global biases for a particular user, and avoiding that can result in a better user experience. The problem is that most deep learning programs aren't intended for (and really be for) one user out there in user space. Deep learning programs are written for the large institutions which have the large troves of data need to train large nets and want to make important decisions using that data. But by this, those decisions won't be made in isolation but will effect a large number of people, people who aren't the users but rather the used. And if such systems have biases relative to the whole, it is a problem. And problem may not be for the institutions immediate goals but for the people who depend on the institution.

haberman 9y ago

I think you make an interesting point: if we use context to train a model to reflect local biases instead of global biases, will that be more just and/or lead to better user experience? It seems related to the question of whether Google results should be tailored to you. If I Google "did Russia interfere in the election", should Google tailor the results so I always see articles that reinforce my world view? If we go that route, I think we take the path of Stephen Colbert's concept of "Truthiness", where we judge something as true because it "feels" true. Users will definitely be happier if everything they see reinforces their existing world view. So companies will be incentivized to accommodate this desire. But does this actually lead to a more just society?