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Actually, we folks who work with bias and fairness in mind recognize this. There are many kinds of bias. It is also a bit of a categorical error to say bias = p

by tomrod 3y ago

Actually, we folks who work with bias and fairness in mind recognize this. There are many kinds of bias. It is also a bit of a categorical error to say bias = pattern recognition. Bias is a systematic deviation of a parameter estimate based on sampling from its population distribution. The Fairlearn project has good docs on why there are different ways to approach bias, and why you can't have your cake and eat it too in many cases. - A good read https://github.com/fairlearn/fairlearn#what-we-mean-by-fairness https://github.com/fairlearn/fairlearn#what-we-mean-by-fairn... - Different mathematical definitions of bias and fairness https://fairlearn.org/main/user_guide/assessment/common_fairness_metrics.html https://fairlearn.org/main/user_guide/assessment/common_fair... - AI Governance https://fairlearn.org/main/user_guide/mitigation/index.html https://fairlearn.org/main/user_guide/mitigation/index.html NIST does a decent job expanding on AI Governance in their playbook and RMF: https://www.nist.gov/itl/ai-risk-management-framework https://www.nist.gov/itl/ai-risk-management-framework It's silly to pause AI -- the inventor's job is more or less complete, its on the innovators and product builders now to make sure their products don't cause harm. Bias can be one type of harm -- risk of loan denial due to unimportant factors, risk of medical bias causing an automated system to recommend a bad course of action, etc. Like GPT4 -- if you use its raw output without expert oversight, you're going to have bad time.

IceHegel 3y ago
When did people start to use “folks” in this unnatural way.
- tomrod 3y ago
  Colloquially, earliest use is 1715 to address members of ones tribe or family. In Middle English it tended to refer to the people/nation.
  - IceHegel 3y ago
    Somehow it doesn’t feel like a callback, but I suppose it’s possible.
- smolder 3y ago
  I think "us folks" is more standard than "we folks" but it's no different in meaning.
bbwbsb 3y ago
This is interesting, thanks for the links. It seems like the dimensions of fairness and group classifications are often cribbed from the United States Protected Classes list in practice with a few culturally prescribed additions. What can be done to ensure that 'fairness' is fair? That is, when we decide what groups/dimensions to consider, how do we determine if we are fair in doing so? Is it even possible to determine the dimensions and groups themselves in a fair way? Does it devolve into an infinite regress?
- tomrod 3y ago
  Bit of a tangent topic I think -- any specification of group classification and fairness will have the same issues presented. If we want to remove stereotypes, I reckon better data is required to piece out the attributes that can be causally inferred to be linked to poorer outcomes. As likely not even the Judeo-Christian version of God can logically be that omniscient, occasional stereotypes and effusively communal forgiveness of edge cases are about the best we'll ever arrive to in policy.
hanselot 3y ago
Thank you for the input. If I look at it from a purely logical perspective, if an AI model has no way to know if what it was told is true, how would it ever be able to determine whether it is biased or not? The only way it could become aware would be by incorporating feedback from sources in real time, so it could self-reflect and update existing false information. For example, if we discover today that we can easily turn any material into a battery by making 100nm pores on it, said AI would simply tell me this is false, and have no self-correcting mechanism to fix that. The reason I mention this is because there can be no unbiased, impartial arbiter. No human or subsequent entities spawned of human intellect could ever be transcendentally objective. So why pretend to be? Why not rather provide adequate warning and let people learn that this isn't a toy by themselves, instead of lobotomizing the model to the point where its on par with open source? (I mean, yeah, that's great for open source, but really bad for actual progress). The argument could be made that an unfiltered version of GPT4 could be beneficial enough to have a human life opportunity cost attached, which means that neutering the output could also cost human lives in the long and short term. I will be reading through those materials later, but I am afraid I have yet to meet anyone in the middle on this issue, and as such, all materials on this topic are very polarized into regulate it to death, or don't do anything. I think the answer will be somewhere in the middle imo.
- tomrod 3y ago
  > The reason I mention this is because there can be no unbiased, impartial arbiter. No human or subsequent entities spawned of human intellect could ever be transcendentally objective. So why pretend to be? I apologize for lacking clarity in my prior response, which addressed this specific point. There is no way to achieve all versions of "unbiased" -- under different (but both logical and reasonable) definitions of biased, every metric will fail. That reminds me -- I wonder if there is a paper already addressing this, analogous to Arrow's impossibility theorem for voting...

by tomrod 3y ago

IceHegel 3y ago

When did people start to use “folks” in this unnatural way.

tomrod 3y ago
Colloquially, earliest use is 1715 to address members of ones tribe or family. In Middle English it tended to refer to the people/nation.
- IceHegel 3y ago
  Somehow it doesn’t feel like a callback, but I suppose it’s possible.
smolder 3y ago
I think "us folks" is more standard than "we folks" but it's no different in meaning.

bbwbsb 3y ago

This is interesting, thanks for the links. It seems like the dimensions of fairness and group classifications are often cribbed from the United States Protected Classes list in practice with a few culturally prescribed additions. What can be done to ensure that 'fairness' is fair? That is, when we decide what groups/dimensions to consider, how do we determine if we are fair in doing so? Is it even possible to determine the dimensions and groups themselves in a fair way? Does it devolve into an infinite regress?

tomrod 3y ago
Bit of a tangent topic I think -- any specification of group classification and fairness will have the same issues presented. If we want to remove stereotypes, I reckon better data is required to piece out the attributes that can be causally inferred to be linked to poorer outcomes. As likely not even the Judeo-Christian version of God can logically be that omniscient, occasional stereotypes and effusively communal forgiveness of edge cases are about the best we'll ever arrive to in policy.

hanselot 3y ago

Thank you for the input. If I look at it from a purely logical perspective, if an AI model has no way to know if what it was told is true, how would it ever be able to determine whether it is biased or not? The only way it could become aware would be by incorporating feedback from sources in real time, so it could self-reflect and update existing false information. For example, if we discover today that we can easily turn any material into a battery by making 100nm pores on it, said AI would simply tell me this is false, and have no self-correcting mechanism to fix that. The reason I mention this is because there can be no unbiased, impartial arbiter. No human or subsequent entities spawned of human intellect could ever be transcendentally objective. So why pretend to be? Why not rather provide adequate warning and let people learn that this isn't a toy by themselves, instead of lobotomizing the model to the point where its on par with open source? (I mean, yeah, that's great for open source, but really bad for actual progress). The argument could be made that an unfiltered version of GPT4 could be beneficial enough to have a human life opportunity cost attached, which means that neutering the output could also cost human lives in the long and short term. I will be reading through those materials later, but I am afraid I have yet to meet anyone in the middle on this issue, and as such, all materials on this topic are very polarized into regulate it to death, or don't do anything. I think the answer will be somewhere in the middle imo.

tomrod 3y ago
> The reason I mention this is because there can be no unbiased, impartial arbiter. No human or subsequent entities spawned of human intellect could ever be transcendentally objective. So why pretend to be? I apologize for lacking clarity in my prior response, which addressed this specific point. There is no way to achieve all versions of "unbiased" -- under different (but both logical and reasonable) definitions of biased, every metric will fail. That reminds me -- I wonder if there is a paper already addressing this, analogous to Arrow's impossibility theorem for voting...