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No, it's not at all the same. Those articles are about eliminating overfitting and improving accuracy. This article is about how much fairness constraints will

No, it's not at all the same. Those articles are about eliminating overfitting and improving accuracy. This article is about how much fairness constraints will reduce your accuracy/objective function.

tptacek 10y ago
Did you read the paper, or just the web page with the simulator? The loan simulator is a motivating example, not the entirety of the work. The work is a comparison between the basic approach of demographic parity and the authors contributions, which seeks to minimize the expense you seem to be talking about.
- yummyfajitas 10y ago
  I read the paper a month ago, and I'm quite familiar with this field for my own reasons [1]. They attempt to minimize the expense I'm talking about. They don't reduce overfitting, improve accuracy, or even make any changes to the underlying predictive algorithm. The simulator is a great illustration of exactly what they did; the entirety of the work is generalizing that to arbitrary predictors (subject to a few conditions) and bounding the accuracy penalty. Feel free to cite the theorem improving accuracy if you disagree. [1] One idea I'm kicking around is the following. Banks/others are legally required to issue bad loans for fairness. I suspect there is a lot of money to be made hacking this, I just haven't figured out how yet.
  - dharma1 10y ago
    I saw a talk by a fintech company CTO who lend to people in European countries without reliable credit scoring. They used lots of data points, effectively a black box system. But sometimes they loan to people they wouldn't normally loan to, just because that loan adds the kind of data they would otherwise not get, and they take a risk on default in exchange for more data. I'm assuming smaller loan amounts. So I guess that is one way for lending institutions to say they are issuing "bad loans for fairness" while still benefiting from it in terms of getting more training data and making the model more robust.

tptacek 10y ago

Did you read the paper, or just the web page with the simulator? The loan simulator is a motivating example, not the entirety of the work. The work is a comparison between the basic approach of demographic parity and the authors contributions, which seeks to minimize the expense you seem to be talking about.

yummyfajitas 10y ago
I read the paper a month ago, and I'm quite familiar with this field for my own reasons [1]. They attempt to minimize the expense I'm talking about. They don't reduce overfitting, improve accuracy, or even make any changes to the underlying predictive algorithm. The simulator is a great illustration of exactly what they did; the entirety of the work is generalizing that to arbitrary predictors (subject to a few conditions) and bounding the accuracy penalty. Feel free to cite the theorem improving accuracy if you disagree. [1] One idea I'm kicking around is the following. Banks/others are legally required to issue bad loans for fairness. I suspect there is a lot of money to be made hacking this, I just haven't figured out how yet.
- dharma1 10y ago
  I saw a talk by a fintech company CTO who lend to people in European countries without reliable credit scoring. They used lots of data points, effectively a black box system. But sometimes they loan to people they wouldn't normally loan to, just because that loan adds the kind of data they would otherwise not get, and they take a risk on default in exchange for more data. I'm assuming smaller loan amounts. So I guess that is one way for lending institutions to say they are issuing "bad loans for fairness" while still benefiting from it in terms of getting more training data and making the model more robust.