epidemiology

Or riding in an uber?

The world runs on capital until it runs on votes, and sometimes bullets.

I think you could say it's inevitable because of the size of both the good AND bad opportunities. Agree with you and the original point of the article that there COULD be a better way. We are reaping tons of bad outcomes across social media, crypto, AI, due to poor leadership(from every side really).

Imagine new coordination technology X. We can remove any specific tech reference to remove prior biases. Say it is a neutral technology that could enable new types of positive coordination as well as negative.

3 camps exist.

A: The grifters. They see the opportunity to exploit and individually gain.

B: The haters. They see the grifters and denigrate the technology entirely. Leaving no nuance or possibility for understanding the positive potential.

C: The believers. They see the grift and the positive opportunity. They try and steer the technology towards the positive and away from the negative.

The basic formula for where the technology ends up is -2(A)-(B) +C. It's a bit of a broad strokes brush but you can probably guess where to bin our current political parties into these negative categories. We need leadership which can identify and understand the positive outcomes and push us towards those directions. I see very little strength anywhere from the tech leaders to politicians to the social media mob to get us there. For that, we all suffer.

Not sure what being personable has to do with knowing how to use a computer.

Let me guess. You already own a home?

This is one of those extremely important points that we say every once in a while and then forget to emphasize. Opposing good ideas or supporting bad ideas because they somehow get tagged into weird ideological buckets along with completely unrelated issues is a big reason why our political system is so dysfunctional.

Wow this is an excellent point and really undermines the article conclusions. We should always be looking at unadjusted scores as well as a whole series of adjusted scores with a variety of methods.

Just grabbing one highly adjusted score and drawing conclusions solely off of that is not enough. It's really only giving you one piece of a very complex puzzle in the case of something like education scores.

Observational stats in social sciences turns out to be a lot like epidemiology and strongly held conclusions are hard won.

China burns more coal than the rest of the world combined.

It's not that we haven't thought up the statistical tools. The core theoretical tools you need are there. It's that gathering the data that you need is extremely difficult and time consuming.

If you gather EHR or medical claims record data for vaccines for example, you have to take very seriously the biases and impact of missingness inherent in the data. Is that person you have no evidence of disease for truly not diseased or do they just have missing data? IS it missing because they just didnt go to the doctor because they're healthy enough to kick the disease on their own or because they're so financially unstable that they can't afford to consistently see their primary care doctor. Is the data missingness itself actually what's more correlated with the disease than the vacciation you are looking at?

Example: If your outcome is dementia then may be using cognitive tests that have a high level of variability due more to social class, education, test taking ability. Is receiving a fancy vaccine is more likely in an affluent area? Could be that correlation itself might completely explain away the positive effect that vaccine has on cognitive test scores.

In Alzheimer's you're often trying to correlate things that happen in early life with long term damage that only surfaces many many years later. Retrospective studies where you go back and ask sick or healthy people have recall bias where the sick ones remember more issues with themselves early on than healthy ones do even with the same early life issues.

Not trying to say epi is perfect or that there isn't room for improvement in tools (there absolutely is). But just like often happens when crossing over into the biological sciences there's a lot stickier problems than people outside the field realize.

>If it's as pivotal as the article suggests

Let's be honest, this is a longshot.

You'll get a lot of haters from the petite bourgeoisie tech landlord crowd here but this is a good idea. The more tools to help renters negotiate against the countless predatory landlords the better. Even better would be ways to report or put their malfeasance on public record. $9 is too expensive though.

Me never wanting to live next door to my landlord caring a lot.

The largest problems with most publications (in epi and in my opinion at least) is study design. Unfortunately, faulty study design or things like data cleaning is qualitative, nuanced, and difficult to catch with AI unless it has access to the source data.

AI tools are hopefully going to eat lots of manual scientific research. This article looks at error spotting, but you follow the path of getting better and better at error spotting to it's conclusion and you essentially reproduce the work entirely from scratch. So in fact AI study generation is really where this is going.

All my work could honestly be done instantaneously with better data harmonization & collection along with better engineering practices. Instead, it requires a lot of manual effort. I remember my professors talking about how they used to calculate linear regressions by hand back in the old days. Hopefully a lot of the data cleaning and study setup that is done now sounds similar to a set of future scientists who use AI tools to operate and check these basic programatic and statistical tasks.