Comment by tomrod | Hacker News Reader

Comment by tomrod | Hacker News Reader

/director hat engaged

I'm really surprised that most of the top level comments are so negative.

This is a huge discussion in using ML for policy. In five to ten years time you'll have a hard time using ML in business without having the concepts these items address.

Will you ML model result in incidental bias to people with criminal records? The disabled? The elderly and other protected categories?

Does your insurance company require certain fairness metrics be deployed to maintain your product or service policy coverage?

Will your company pursue tax incentives that focus on certain definitions of unbiasedness?

Developers, data scientists, and statisticians have had a good run implementing loads of statistical models without considering the negative implications/externalities of automated decision rules and paths to KPIs. We will likely see this required in the future.

It will be hard. But, it will be hard to automate away for the foreseeable future (Github Copilot made a serious POC impact for standard dev work!).

/throws director hat back onto the hat rack, back to data science for me

If an elderly person will cost your insurance more than a younger person, the ML should tell you so. Period.

If by policy you should be forced to still give them the same conditions, that should be decided on a different level, not at the level of the tool that analyzes your data.

> I'm really surprised that most of the top level comments are so negative.

Lots of throwaway accounts here, and the comments are clearly written by people who aren't professionals in this field. Just downvote them and move on.

> Does your insurance company require certain fairness metrics be deployed to maintain your product or service policy coverage? Will your company pursue tax incentives that focus on certain definitions of unbiasedness?

These are scary thoughts. If this kind of policy does come to pass, I fear it would backfire significantly, basically making any machine learning (let alone "AI") a serious legal liability. Imagine world in which companies no longer want to hire data scientists of the regulatory risk of even having one on staff!

There's an argument to be made that a bit of ML liability is a good thing, that important decisions shouldn't be made by machine. I'm tempted to agree in most cases. But encoding it into regulation will be extremely difficult, even if you have truly pure and noble intentions, without making a giant mess. And that's before you realize that, obviously, major "AI powers" like Google, Facebook, etc. could have been working on this for years, and as soon as it's ready for production they will start lobbying aggressively for it, in order to further monopolize the ML/AI field.

Also, do the machines actually perform worse (i.e. "more biased") than underpaid humans who are prone to fatique, mistakes, and laziness?

How is anything you just described different, in any way, from the existing situation in risk assessment and fairness in insurance? IE, everything we say about ML, the insurance business has already been doing for a century using statistics. Their models exist for two things: to increase the likelihood of profit (due to properly determining rates based on risk) and staying compliant (if there are laws that ensure some level of fairness).

Frankly I don't see any real difference from long-established principles in the insurance space.

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