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> Nothing here is morally biased: neither the algorithm or the data. I think the issue here is the following progression: 1. Someone chooses and collects a su
by djoldman 5y ago
> Nothing here is morally biased: neither the algorithm or the data.
I think the issue here is the following progression:
1. Someone chooses and collects a subset of all data that exists.
2. They then choose an algorithm/generic-framework-of-a-model with the intent to apply it to the data chosen in step 1.
3. They then train the chosen algorithm from step 2, yielding a model.
What @darawk is saying, I believe, is that the algorithm/generic-framework-of-a-model chosen in step 2 is not morally/ethically biased/racist/etc. A human may consider the outputted model from step 3 to be so, however.
If the above is true, then the source of moral/ethical bias is the data chosen in step 1.
What untrained model/algorithm is morally or ethically biased? I know of no model listed in scikit learn, tensorflow, scipy, etc. that has hardcoded any sense of anything about humans at all...
- seoaeu 5y agoI don't see how that follows. Surely you could select hyper-parameters that cause a model to perform differently across different races, ethnicities, etc. Like, shouldn't you be able to get a model to perform poorly for minority groups simply by specifying a model size that's too small to learn the full data set, with the knowledge that most training samples won't be describing those groups?
- djoldman 5y agoHrm, I think I understand what you're saying. Perhaps a better way to formulate it is: let's say we take a model and add a regularization term that penalizes/rewards some part of the data, which when trained makes for a morally/ethically biased model. I think you're exactly correct, the source of moral/ethical bias here is in the model. Good point.