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Yeah, but it is indirectly the biases of the researchers. A researcher is more likely to notice and correct for training data problems that conflict with their

Yeah, but it is indirectly the biases of the researchers. A researcher is more likely to notice and correct for training data problems that conflict with their biases.

kortilla 6y ago
People doing ML “research” are not the ones applying it to specific data sets day to day. “I pointed a neural net at our sales data” is not research in the normal sense.
- cbsmith 6y ago
  Yeah, I look at the semantic problem with calling it "ML research" and just throw up my arms. These discussions aren't generally driven by people who care about semantics.
hans1729 6y ago
This requires him to be aware of each pattern/“bias” in the data, which he isn’t, which is the reason we use the algos in the first place. Come to think of it, isn’t that an interesting venue for GAN-esque methods to detect the relation of patterns falling into these categories of biases? Or is that recursive problem? If not, put me in the paper :-)
- cbsmith 6y ago
  > This requires him to be aware of each pattern/“bias” in the data, which he isn’t, which is the reason we use the algos in the first place. That's not strictly true. In a lot of cases you start out oblivious to biases in the data, and then when you evaluate the model you notice problems. But your point about obliviousness to bias is exactly what I'm speaking towards. One might be oblivious to bias that aligns with your own biases, but notice bias that conflicts with it.
- adjkant 6y ago
  I can't tell if watching the switch from they to he pronouns in a post about bias awareness is making me side more with or against your point :)
  - hans1729 6y ago
    Lets just say I made the consicious descision to go with the masculine form :-)
visarga 6y ago
> A researcher is more likely to notice and correct for training data problems that conflict with their biases. That's not a systematic way of tackling bias. I would rather have invested more in creating good benchmarks and norms.
- cbsmith 6y ago
  I would very much agree it is not a systematic way of tackling bias. That's why it creates bias. ;-)

Yeah, but it is indirectly the biases of the researchers. A researcher is more likely to notice and correct for training data problems that conflict with their biases.

kortilla 6y ago

People doing ML “research” are not the ones applying it to specific data sets day to day. “I pointed a neural net at our sales data” is not research in the normal sense.

cbsmith 6y ago
Yeah, I look at the semantic problem with calling it "ML research" and just throw up my arms. These discussions aren't generally driven by people who care about semantics.

hans1729 6y ago

This requires him to be aware of each pattern/“bias” in the data, which he isn’t, which is the reason we use the algos in the first place. Come to think of it, isn’t that an interesting venue for GAN-esque methods to detect the relation of patterns falling into these categories of biases? Or is that recursive problem? If not, put me in the paper :-)

cbsmith 6y ago
> This requires him to be aware of each pattern/“bias” in the data, which he isn’t, which is the reason we use the algos in the first place. That's not strictly true. In a lot of cases you start out oblivious to biases in the data, and then when you evaluate the model you notice problems. But your point about obliviousness to bias is exactly what I'm speaking towards. One might be oblivious to bias that aligns with your own biases, but notice bias that conflicts with it.
adjkant 6y ago
I can't tell if watching the switch from they to he pronouns in a post about bias awareness is making me side more with or against your point :)
- hans1729 6y ago
  Lets just say I made the consicious descision to go with the masculine form :-)

visarga 6y ago

> A researcher is more likely to notice and correct for training data problems that conflict with their biases. That's not a systematic way of tackling bias. I would rather have invested more in creating good benchmarks and norms.

cbsmith 6y ago
I would very much agree it is not a systematic way of tackling bias. That's why it creates bias. ;-)