[eksemplar]

I think there is an important lesson in the approach to science that we see in the social sciences. Coming from a technical background we approach science, and data, as being the fundamental way of discover truth, but with humans there are often more than one truth.

We’ve seen the effect of it in management over the past 25 years. Today a good manager is expected to approach a team, not by instructing them in what to do and when to do it, but rather by creating a shared meaning through group conversation. It’s more important when you manage people who produce by thinking and being creative, but even at the factory line, this softer approach is proving useful.

We haven’t yet applied this to big data. I’m often sold ML as the ability to predict the future, and to some extend that is true. If I look at all the alcoholic families in my municipality and compare their case history with big data gathered on a national level, I’ll certainly be able to predict how many of their children we’ll need to remove. I just can’t predict which ones because determinism doesn’t actually work on something that complex.

The more data we have the less we understand about causality, something I’ve learned from history. If you look at the Roman Empire without digging into it, chosing Christianity seem obvious, but if you really get all the data on their options and then try to figure out why they did like they did, you’ll have no clue. Another example is online advertising, I read a news paper that I’ve never seen a single add for, and I see a lot of adds for news papers. I’m often called by news paper salesmen as well, but not for the one I read. This is because it doesn’t suit my elaborate online profile. My profile tells the add agencies what I should read, but it doesn’t tell them why, and the difference is failing them.

If we really want ML and big data to be truely useful, I think we need to learn from the social sciences, because they work much more with the complicated science behind the why.

[zaphar]

Except that they still can't do what the author needs in order to do her work. A tool she can use. Social Sciences have not yet produced anything that can effectively produce a change or even adequately describe a social system.

Her tagline is appropriate. She'll gladly use a tool that works, but she won't use one on faith.

[eksemplar]

You're right, but I think my point is that data science hasn't really done anything useful in fields involving humans.

Don't get me wrong, we do a lot of data science in the public sector these years, but we also measure it's efficiency and capability and compare it to the past 100 years of us doing the same thing without AI, and things haven't improved. At least not yet.

Mean while, the social sciences have given us tool that help us inflict actual lasting change on groups of people simply by using language in a specific way or working toward a shared consensus.

So maybe the question shouldn't be what social sciences have to offer AI research, but rather, what data science has to offer social fields.

I'm well aware that data science has it's value in other fields. We use it to troll through massive amounts case files and save thousands of man-hours in the process, but why would you want to use social science for that?

[nonbel]

>"data science hasn't really done anything useful in fields involving humans."

So you think data science has not been useful to google, amazon, facebook, etc?

Basically you are proposing that (at least) hundreds of billions of dollars have been spent with no return so far. It is possible but is there evidence for this?

[nonbel]

>"If I look at all the alcoholic families in my municipality and compare their case history with big data gathered on a national level, I’ll certainly be able to predict how many of their children we’ll need to remove. I just can’t predict which ones because determinism doesn’t actually work on something that complex. [...] I think we need to learn from the social sciences, because they work much more with the complicated science behind the why."

Huh? ML classifiers will definitely give you a prediction for each individual case. Its the social sciences that have been choosing to look at an average effect at one single timepoint, etc and trying to get some kind of causal model from that (a dumb idea in my opinion since causality is working at the individual level).

EDIT:

I should also say I am open to the idea that causality isn't a real, or at least interesting, thing anyway. Eg PV = nRT, does that mean changing pressure changes the temperature or vice versa?

[SiempreViernes]

The gas law is a equilibrium condition, so if you change one the others must change too. But they don’t change spontaneously, you impose the change from without, so there is no ambiguity: whatever you force to change first will be the cause of the others changing.

[nonbel]

Right, the model accounts for all possible "causal routes", turning causality into something subjective.