4 ms·

In most fields of human endeavor you do have an objective function better than "do what humans do". Mortgages are very specifically an area where you do. First

In most fields of human endeavor you do have an objective function better than "do what humans do". Mortgages are very specifically an area where you do. First of all, there is historical data. Second of all, you can backtest well before 25 years. A couple of weeks ago I wrote a blog post explaining specifically how to make measurements in the presence of delayed reactions - I'm discussing a situation involving sensor networks, literally the same mathematics would work for mortgage default or refinance: https://www.chrisstucchio.com/blog/2016/delayed_reactions.html https://www.chrisstucchio.com/blog/2016/delayed_reactions.ht... Third, mortgage lenders can often backtest alternate decisions because there are pretty straightforward relationships between decisions. Some of them are even mechanistic, e.g. refinance_risk(interest_rate) and default_prob(interest_rate) are monotonically increasing. You seem to think we are living in the exact specific dark age necessary to make your morality play poignant and relevant. That's about as silly as Star Trek landing on all sorts of alien planets, each one designed to highlight one specific social issue from USA 1966.

lmm 10y ago
There's no historical data on the default rates of people who didn't get mortgages, because they didn't get mortgages. I'm willing to believe that theoretical solutions exist. I know from direct personal experience in the big data/lending industry that they are not always applied. If you are claiming that real-world lenders never train their models on human decisions then you are simply wrong. Nice zinger - I hope you weren't saving it for too long. But I'm not going to get into witticisms or personal arguments.
- ves 10y ago
  To my knowledge, more sophisticated lenders will let through loans that have poor model scores to fill in that data for training future models.
- yummyfajitas 10y ago
  Sure, but there is plenty of historical data on people who marginally got mortgages. If those people are solidly profitable, as opposed to marginally profitable, you try a marginally more expansive lending criteria. I.e., if I know f(0) = 10, f(1) = 9, f(2) = 8, but I don't have data on f(3), it's worth running a bit of an experiment to see if f(3) is actually 7. (I happen to know from experience I can't talk about that this analysis is regularly done, albeit keeping the Lucas Critique in mind.) I don't claim that real world use of ML is perfect. I claim that "bias" (in the sense of making wrong decisions due to race) is a statistical problem and the solution is simply better algorithms rather than Cathy O'Neil's statistical nihilism.
  - rlucas 10y ago
    You're right within the boundaries of what you're considering, meaning testing the profitability of marginal borrowers, etc. But you're missing the big picture question about the use of ML in making political decisions (and yes, the question of who gets mortgages is very much a political one). For example, in the earliest days of the creation of the US national mortgage market, conforming to FHA standards meant following more or less explicit racial guidelines (not to mention car-promoting platting and urban planning guidelines). That amounted to a political decision to reward people who invested (and/or resided) in white suburbia with greater liquidity and hence price appreciation. Focusing on the marginal profitability of individual loans ignore the bigger issue, which is that choosing the rules often encodes a much larger set of political preferences or goals. Then, the critique of using opaque algos is, for me, less that they hide the use of some verboten input variable like race or sex, but that by focusing on the algos we ignore the bigger question. (it's a subclass of what I call the scalar fallacy, where any real world outcome being reduced to a scalar always collapses a multidimensional space into a single dimension, and that projection contains a huge set of silent preferences)
    - yummyfajitas 10y ago
      I'm aware that political processes surrounding loans were racially biased. That's not evidence that a profit maximizing algorithm will be. That's really just a claim that "this was once politicized, therefore it must remain political forever". The whole benefit of using algorithms is that you make your goals explicit and non-opaque; you just look at the objective function. Opacity of the models isn't really a big deal - machine learning is fundamentally the mathematics of studying opaque models. It's the objective function that should be transparent. If the objective function says "minimize defaults", that's your explicit goal. If the objective function says "maximize # of blacks getting loans", that's what you'll do. But it will do one or the other, unless you explicitly set your objective function to a x "minimize defaults" + b x "maximize blacks". The tricky bit is that by doing this, we are explicitly and verifiably lowering the bar. Just witness how much flak people get for discussing this: https://techcrunch.com/2015/11/03/twitter-engineering-manager-leslie-miley-leaves-company-because-of-diversity-issues/ https://techcrunch.com/2015/11/03/twitter-engineering-manage... But unfortunately, an optimization system won't give you what you want unless you explicitly put what you want in the objective function, something our modern activists (probably including Cathy O'Neil) refuse to do.

lmm 10y ago

There's no historical data on the default rates of people who didn't get mortgages, because they didn't get mortgages. I'm willing to believe that theoretical solutions exist. I know from direct personal experience in the big data/lending industry that they are not always applied. If you are claiming that real-world lenders never train their models on human decisions then you are simply wrong. Nice zinger - I hope you weren't saving it for too long. But I'm not going to get into witticisms or personal arguments.

ves 10y ago
To my knowledge, more sophisticated lenders will let through loans that have poor model scores to fill in that data for training future models.
yummyfajitas 10y ago
Sure, but there is plenty of historical data on people who marginally got mortgages. If those people are solidly profitable, as opposed to marginally profitable, you try a marginally more expansive lending criteria. I.e., if I know f(0) = 10, f(1) = 9, f(2) = 8, but I don't have data on f(3), it's worth running a bit of an experiment to see if f(3) is actually 7. (I happen to know from experience I can't talk about that this analysis is regularly done, albeit keeping the Lucas Critique in mind.) I don't claim that real world use of ML is perfect. I claim that "bias" (in the sense of making wrong decisions due to race) is a statistical problem and the solution is simply better algorithms rather than Cathy O'Neil's statistical nihilism.
- rlucas 10y ago
  You're right within the boundaries of what you're considering, meaning testing the profitability of marginal borrowers, etc. But you're missing the big picture question about the use of ML in making political decisions (and yes, the question of who gets mortgages is very much a political one). For example, in the earliest days of the creation of the US national mortgage market, conforming to FHA standards meant following more or less explicit racial guidelines (not to mention car-promoting platting and urban planning guidelines). That amounted to a political decision to reward people who invested (and/or resided) in white suburbia with greater liquidity and hence price appreciation. Focusing on the marginal profitability of individual loans ignore the bigger issue, which is that choosing the rules often encodes a much larger set of political preferences or goals. Then, the critique of using opaque algos is, for me, less that they hide the use of some verboten input variable like race or sex, but that by focusing on the algos we ignore the bigger question. (it's a subclass of what I call the scalar fallacy, where any real world outcome being reduced to a scalar always collapses a multidimensional space into a single dimension, and that projection contains a huge set of silent preferences)
  - yummyfajitas 10y ago
    I'm aware that political processes surrounding loans were racially biased. That's not evidence that a profit maximizing algorithm will be. That's really just a claim that "this was once politicized, therefore it must remain political forever". The whole benefit of using algorithms is that you make your goals explicit and non-opaque; you just look at the objective function. Opacity of the models isn't really a big deal - machine learning is fundamentally the mathematics of studying opaque models. It's the objective function that should be transparent. If the objective function says "minimize defaults", that's your explicit goal. If the objective function says "maximize # of blacks getting loans", that's what you'll do. But it will do one or the other, unless you explicitly set your objective function to a x "minimize defaults" + b x "maximize blacks". The tricky bit is that by doing this, we are explicitly and verifiably lowering the bar. Just witness how much flak people get for discussing this: https://techcrunch.com/2015/11/03/twitter-engineering-manager-leslie-miley-leaves-company-because-of-diversity-issues/ https://techcrunch.com/2015/11/03/twitter-engineering-manage... But unfortunately, an optimization system won't give you what you want unless you explicitly put what you want in the objective function, something our modern activists (probably including Cathy O'Neil) refuse to do.