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You entirely miss the point! The point is that in supervised learning for example, if you optimize prediction accuracy with respect to your human generated exam

You entirely miss the point! The point is that in supervised learning for example, if you optimize prediction accuracy with respect to your human generated examples, you will get a model that exactly reproduces the racist judgment of the human that generated your training set.

mcguire 10y ago
Yes, and you can get similar effects with unsupervised learning if the data set is biased.
Houshalter 10y ago
Thats silly though. You should use ground truth of actual outcomes, not predict that useless humans would do. But even then I bet the algorithm would be less racist than the humans, if you don't give them race as a feature.
hx87 10y ago
Then the problem is in the racist human(s), not the algorithm.
- yoo1I 10y ago
  But the algorithm enshrines and possibly amplifies it. Or as the old saying goes: > To err is human, to really foul things up requires a computer.
  - Symmetry 10y ago
    Humans have much more potential for racism in making predictions about the future than in judging things that have already happened. So while I agree that racism through biased input data is a problem, I think that even with that problem machines should be substantially less racist in their judgement than the humans they're replacing even if they're not perfect.
AnthonyMouse 10y ago
> The point is that in supervised learning for example, if you optimize prediction accuracy with respect to your human generated examples, you will get a model that exactly reproduces the racist judgment of the human that generated your training set. In which case you aren't optimizing prediction accuracy. Prediction accuracy is measured by whether the predictions are true. If you have bias in the predictions which doesn't exist in the actual outcomes then there is money to be made by eliminating it. It seems like the strangest place to put an objection where the profit motive is directly aligned with the desired behavior.
- wodenokoto 10y ago
  You need to think about how we measure truth and even what truth is. In machine learning we tend to assume the annotations and labels are "true" and build a system towards that version of the "truth". > Prediction accuracy is measured by whether the predictions are true. The more I think about this sentence, the less sense it makes. Prediction accuracy can only be measured against records of something, and that record will be a distortion and simplification of reality.
  - AnthonyMouse 10y ago
    > Prediction accuracy can only be measured against records of something, and that record will be a distortion and simplification of reality. Prediction accuracy can be measured against what actually happens. If the algorithm says that 5% of people like Bob will default and you give loans to people like Bob and 7% of them default then the algorithm is off by 2%.
    - wodenokoto 10y ago
      You are still assuming everything that is recorded to be "like Bob" is the truth and captures reality clearly. Moreover you need to give loans to everybody in order to check the accuracy of the algorithm. You can't just check a non random subset and expect to get non biased results.
deleted 10y ago
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mcguire 10y ago

Yes, and you can get similar effects with unsupervised learning if the data set is biased.

Houshalter 10y ago

Thats silly though. You should use ground truth of actual outcomes, not predict that useless humans would do. But even then I bet the algorithm would be less racist than the humans, if you don't give them race as a feature.

hx87 10y ago

Then the problem is in the racist human(s), not the algorithm.

yoo1I 10y ago
But the algorithm enshrines and possibly amplifies it. Or as the old saying goes: > To err is human, to really foul things up requires a computer.
- Symmetry 10y ago
  Humans have much more potential for racism in making predictions about the future than in judging things that have already happened. So while I agree that racism through biased input data is a problem, I think that even with that problem machines should be substantially less racist in their judgement than the humans they're replacing even if they're not perfect.

AnthonyMouse 10y ago

> The point is that in supervised learning for example, if you optimize prediction accuracy with respect to your human generated examples, you will get a model that exactly reproduces the racist judgment of the human that generated your training set. In which case you aren't optimizing prediction accuracy. Prediction accuracy is measured by whether the predictions are true. If you have bias in the predictions which doesn't exist in the actual outcomes then there is money to be made by eliminating it. It seems like the strangest place to put an objection where the profit motive is directly aligned with the desired behavior.

wodenokoto 10y ago
You need to think about how we measure truth and even what truth is. In machine learning we tend to assume the annotations and labels are "true" and build a system towards that version of the "truth". > Prediction accuracy is measured by whether the predictions are true. The more I think about this sentence, the less sense it makes. Prediction accuracy can only be measured against records of something, and that record will be a distortion and simplification of reality.
- AnthonyMouse 10y ago
  > Prediction accuracy can only be measured against records of something, and that record will be a distortion and simplification of reality. Prediction accuracy can be measured against what actually happens. If the algorithm says that 5% of people like Bob will default and you give loans to people like Bob and 7% of them default then the algorithm is off by 2%.
  - wodenokoto 10y ago
    You are still assuming everything that is recorded to be "like Bob" is the truth and captures reality clearly. Moreover you need to give loans to everybody in order to check the accuracy of the algorithm. You can't just check a non random subset and expect to get non biased results.

deleted 10y ago

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