4 ms·

I still don't get what solution they are proposing to the problem. I have also always assumed that any bias stems from data since the system uses it to learn. W

I still don't get what solution they are proposing to the problem. I have also always assumed that any bias stems from data since the system uses it to learn. What's the alternative?

empath75 6y ago
Deciding what is fair and what is biased is a political question that impacts a lot of people who aren’t software engineers and it shouldn’t be up to them to decide.
alecbenzer 6y ago
Yeah, I think that's what's missing for me too. Like, what do they expect LeCun to be doing instead? I guess it's possible to suggest that, given the lack of diversity in training sets, different training techniques should be adopted to account for it. Is that actually a viable approach that people are suggesting, and LeCun is ignoring/downplaying?
aiaiai 6y ago
Hire minorities to work on your machine learning models. At the very least it ensures you won't 'forget' to train the model with black faces.
- MaximumYComb 6y ago
  I'm not from the US but there is a link between socioeconomic status and education. I would assume that with the large percentage of minorities, especially black Americans, raised in poverty that there would be a lack of suitable candidates. Just one of the ways that history of racism affects the modern world. Do they have any practical suggestions on where to find a huge number of suitable qualified people? The people in prestigious academic positions often come from not just non-poverty backgrounds but wealthy backgrounds.
  - aiaiai 6y ago
    Facebook is rich enough to invest in black people's education and essentially train data scientists from the ground up. A black scholarship for data science would be a great gesture from them. Plenty of other industries do this for their fields as well.
- lurkmurk 6y ago
  Well educated people who can work on this problem are not minorities, i.e. they have access to education, higher social status, better starting point. While their color of skin might be different, they don't need to be aware of all the issues their race/sex faces. Example can be difference between treatment of certain minorities in US (where most of the science is developed) and the rest of the world. A scientist at Facebook/Google will develop something for the whole world having US centric view, without access to views from other cultures and their reqiurements for fairness.
stonogo 6y ago
I wrote up a bit of a lengthy comment that hopefully sheds some light on this question: https://news.ycombinator.com/item?id=23697191 https://news.ycombinator.com/item?id=23697191 If I had to summarize it in a sentence, the solution proposed is "consider the ethical implications of misapplication of your work, and attempt up front to mitigate the damage that would result." It's more nuanced than that, but it's the reason some people don't accept Yann LeCun's "just debias the inputs" as sufficient.
icelancer 6y ago
>> I still don't get what solution they are proposing to the problem. They aren't proposing one. Most people would rather complain than fix.
- deleted 6y ago
  [deleted]

I still don't get what solution they are proposing to the problem. I have also always assumed that any bias stems from data since the system uses it to learn. What's the alternative?

empath75 6y ago

Deciding what is fair and what is biased is a political question that impacts a lot of people who aren’t software engineers and it shouldn’t be up to them to decide.

alecbenzer 6y ago

Yeah, I think that's what's missing for me too. Like, what do they expect LeCun to be doing instead? I guess it's possible to suggest that, given the lack of diversity in training sets, different training techniques should be adopted to account for it. Is that actually a viable approach that people are suggesting, and LeCun is ignoring/downplaying?

aiaiai 6y ago

Hire minorities to work on your machine learning models. At the very least it ensures you won't 'forget' to train the model with black faces.

MaximumYComb 6y ago
I'm not from the US but there is a link between socioeconomic status and education. I would assume that with the large percentage of minorities, especially black Americans, raised in poverty that there would be a lack of suitable candidates. Just one of the ways that history of racism affects the modern world. Do they have any practical suggestions on where to find a huge number of suitable qualified people? The people in prestigious academic positions often come from not just non-poverty backgrounds but wealthy backgrounds.
- aiaiai 6y ago
  Facebook is rich enough to invest in black people's education and essentially train data scientists from the ground up. A black scholarship for data science would be a great gesture from them. Plenty of other industries do this for their fields as well.
lurkmurk 6y ago
Well educated people who can work on this problem are not minorities, i.e. they have access to education, higher social status, better starting point. While their color of skin might be different, they don't need to be aware of all the issues their race/sex faces. Example can be difference between treatment of certain minorities in US (where most of the science is developed) and the rest of the world. A scientist at Facebook/Google will develop something for the whole world having US centric view, without access to views from other cultures and their reqiurements for fairness.

stonogo 6y ago

I wrote up a bit of a lengthy comment that hopefully sheds some light on this question: https://news.ycombinator.com/item?id=23697191 https://news.ycombinator.com/item?id=23697191 If I had to summarize it in a sentence, the solution proposed is "consider the ethical implications of misapplication of your work, and attempt up front to mitigate the damage that would result." It's more nuanced than that, but it's the reason some people don't accept Yann LeCun's "just debias the inputs" as sufficient.

icelancer 6y ago

>> I still don't get what solution they are proposing to the problem. They aren't proposing one. Most people would rather complain than fix.

deleted 6y ago
[deleted]