PCA probably means Principal Component Analysis, if anybody wonders.

https://en.m.wikipedia.org/wiki/Principal_component_analysis

Thanks :)

In lots of organizations sets of metrics aren't "held in tension" and that causes all sorts of wild problems. As a trivial example say you want to stop people from quitting your app. Easy! Just lower the price by 50%. So we often have to set metrics while satisfying all the other constraints. This is the #1 thing I've seen go wrong when teams try to use OKRs or some other metrics based activity; they fail to figure out the related components and either hold those in place or improve them along side their initial goals.

This is a great illustration of the limits of AI — the scope of available data is limited. The data available in medical records is both limited and often garbage.

Without the context of behavioral data and other factors, it’s dangerous to draw conclusions.

In other scenarios, like say predicting the likelihood that a mole is cancerous, AI will eventually be better and more consistent than a human.

What decision making process if not a statistical one would be better when it faces the same data limitations? Or what if we know the best decision making process for a given context, but it’s too expensive to apply?

For example, we may know that evaluating loan applicants is best done with in-person interviews, but it’s simply too costly and delays getting loans to people too long, so we agree to use an automated decision process knowing the cost of it making some mistakes will be part of what is paid for the benefit of being able to process many applications quickly.

> what decision making process if not a statistical

I believe it's called eye-balling. You'll be suprised how good farmers are at this. It's also why I think tech in agriculture is something of an over-sell; farm managers are already some of the most efficient people you'll meet and even a notebook is sometimes too much to ask for them.

Edit: To answer your question with more elaboration: A statistical model is a proxy for not having full knowledge of the subject matter. But any deep analysis of the subject matter will require personal attention. Farmers (to quote my example again) are specifically good because of their personal knowledge of the farm and the sheer amount of time spent working.

If a predictive process discards risk for humans who lack access to healthcare, it’s creating an obvious ethical problem.

There’s a big difference between writing a loan and missing a stroke.

That seems more like an argument to make more data available. Presumably more people at risk of a stroke can be notified by an algorithmic assessment than could be possible trying to fit them all through actual physician screenings, no? In a public health sense, it would do more good in terms of lives saved, even if algorithmic errors cost lives in some specific cases.

I’m familiar with programs that at scale that have been demonstrated to predict certain negative health/behavior patterns.

From a medical point of view, you can assume a 10-20% probability of a negative outcome. If you layer on other factors or behaviors in the persons life, the probability of a bad outcome approaches 90%.

Problem is, you need deeply intrusive data at scale. What are the consequences of a organization with Google/Facebook capability who also is your AI doctor? It’s easy to foresee complications.

Just to piggyback off this, one should be very careful combining techniques with PCA. One error I see a lot in biostatistics is people running statistical significance testing to select variables before running PCA. It's a super cool exercise to do that on datasets, because something WILL show up, and it's very often misleading.