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Ok, a lot to unpack here. The viewpoint I am presenting is not novel and is what the paper is about. These “academic” notions of fairness are things you can pro

by mirker 5y ago

Ok, a lot to unpack here. The viewpoint I am presenting is not novel and is what the paper is about. These “academic” notions of fairness are things you can prove for models, unlike ad-hoc variants that “feel” fair. So it’s not “my” viewpoint, but the broader “academic” viewpoint. If you look at section 5.2 of this paper and re-read my post, you will probably better understand what I mean https://arxiv.org/pdf/1912.06883.pdf https://arxiv.org/pdf/1912.06883.pdf A very similar example is discussed in 6.2 of https://arxiv.org/pdf/1104.3913.pdf https://arxiv.org/pdf/1104.3913.pdf, which is a seminal work in the field. Also see Appendix A point 2 for more discussion on encoding race/sex in correlations. My top-level post was asking what we care about, which is in-line with the running example. I doubt society really cares too much about fairness in running vs. say insurance claims and race. My point is that even with a perfect correction model, you still need to agree on what correction to apply. And yeah, for DNA, I agree with what you said. But if you use the same “unfair” model for non-personal use, you very quickly start running a eugenics program (e.g., deny healthcare for all people with a marker for cancer), which is generally viewed as bad. Basically, if you aren’t impacting society (e.g., personal use), then fairness is irrelevant.

mattmcknight 5y ago
I am proposing a notion of fairness that you can prove. I am arguing that the other notion of fairness is not a useful measure of model fairness because it results in lower model performance and has negative real world consequences when employed. The example in 5.2 of the Binns paper suggests a definition of fair where the results of model have to be the same for different groups. None of the obvious problems are identified. If you take group A and B, with average income of 100,000 and 50,000 respectively, this fairness definition suggests that a loan approval model for Group A and B should have the same approval rate for a 50,000 loan in order to be fair. If this model were used, the model would be less accurate in predicting repayment than a model that did not attempt to correct for group disparities, Group B would default at a much higher rate and suffer the negative consequences to their credit. This notion of fairness denies the reality of the situation. If there is a group difference in ability to repay loans, it is not the job of the model to pretend that this difference does not exist and make decisions that will negatively affect the financial position of all concerned. The mention in 6.2 of the second paper didn't convey much on this topic, but they do admit "statistical parity is insufficient as a general notion of fairness." It does even take it back to Rawls, one philosopher I find most offensive. I was hopeful when they started with "We capture fairness by the principle that any two individuals who are similar with respect to a particular task should be classified similarly." Yet, they then dissolve this only having this condition hold within a subgroup, such that similar members of group A are treated similarly, and similar members of group B are treated similarly, but a member of group A that is similar to a member of group B is not treated similarly. The notion of fairness I advocate is that the selected features have to predictive, independent of group. Thus, the fairness measure for whether income could be included in the model would be whether income was predictive for group A and group B. If you took the data for group A and income was predictive and then then the data for group B and income was predictive, it would indicate that it was a valid measure for each groups. If you had a feature that was not predictive for each group, it would fail this fairness test and could not include it in an uncorrected model used for all groups. In terms of what we care about, I find it vastly better to discuss these topics with things we don't care much about. However, if we discover that driver safety courses really improve driver safety and lead to lower insurance costs, creating "fairness" by faking the results so that those who cannot afford these courses are given artificially lower insurance rates to compensate for this, we are actually being unfair to the society that would benefit from better drivers, rather than attempting to correct the underlying issue by subsidizing the cost of the course for drivers that cannot afford it.
- mirker 5y ago
  The notion of fairness you are discussing is 1) vague 2) very similar to standard training. If you have an optimization problem that represents your fairness, it’d be more useful to just compare that with the other problems in the literature. Otherwise, it sounds like you want max accuracy and max fairness, which is unlikely to occur on non-degenerate datasets. Anyway, if I’m understanding correctly, you would end up with “individual fairness” if you did formalize your fairness, which is discussed in the linked papers.

by mirker 5y ago

mattmcknight 5y ago

I am proposing a notion of fairness that you can prove. I am arguing that the other notion of fairness is not a useful measure of model fairness because it results in lower model performance and has negative real world consequences when employed. The example in 5.2 of the Binns paper suggests a definition of fair where the results of model have to be the same for different groups. None of the obvious problems are identified. If you take group A and B, with average income of 100,000 and 50,000 respectively, this fairness definition suggests that a loan approval model for Group A and B should have the same approval rate for a 50,000 loan in order to be fair. If this model were used, the model would be less accurate in predicting repayment than a model that did not attempt to correct for group disparities, Group B would default at a much higher rate and suffer the negative consequences to their credit. This notion of fairness denies the reality of the situation. If there is a group difference in ability to repay loans, it is not the job of the model to pretend that this difference does not exist and make decisions that will negatively affect the financial position of all concerned. The mention in 6.2 of the second paper didn't convey much on this topic, but they do admit "statistical parity is insufficient as a general notion of fairness." It does even take it back to Rawls, one philosopher I find most offensive. I was hopeful when they started with "We capture fairness by the principle that any two individuals who are similar with respect to a particular task should be classified similarly." Yet, they then dissolve this only having this condition hold within a subgroup, such that similar members of group A are treated similarly, and similar members of group B are treated similarly, but a member of group A that is similar to a member of group B is not treated similarly. The notion of fairness I advocate is that the selected features have to predictive, independent of group. Thus, the fairness measure for whether income could be included in the model would be whether income was predictive for group A and group B. If you took the data for group A and income was predictive and then then the data for group B and income was predictive, it would indicate that it was a valid measure for each groups. If you had a feature that was not predictive for each group, it would fail this fairness test and could not include it in an uncorrected model used for all groups. In terms of what we care about, I find it vastly better to discuss these topics with things we don't care much about. However, if we discover that driver safety courses really improve driver safety and lead to lower insurance costs, creating "fairness" by faking the results so that those who cannot afford these courses are given artificially lower insurance rates to compensate for this, we are actually being unfair to the society that would benefit from better drivers, rather than attempting to correct the underlying issue by subsidizing the cost of the course for drivers that cannot afford it.

mirker 5y ago
The notion of fairness you are discussing is 1) vague 2) very similar to standard training. If you have an optimization problem that represents your fairness, it’d be more useful to just compare that with the other problems in the literature. Otherwise, it sounds like you want max accuracy and max fairness, which is unlikely to occur on non-degenerate datasets. Anyway, if I’m understanding correctly, you would end up with “individual fairness” if you did formalize your fairness, which is discussed in the linked papers.