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AI

Researchers find AI is Bad at Predicting GPA, Grit, Eviction, Job Training, Layoffs, and Material Hardship (venturebeat.com) 63

Posted by msmash from the closer-look dept.
A paper coauthored by over 112 researchers across 160 data and social science teams found that AI and statistical models, when used to predict six life outcomes for children, parents, and households, weren't very accurate even when trained on 13,000 data points from over 4,000 families. From a report: They assert that the work is a cautionary tale on the use of predictive modeling, especially in the criminal justice system and social support programs. "Here's a setting where we have hundreds of participants and a rich data set, and even the best AI results are still not accurate," said study co-lead author Matt Salganik, a professor of sociology at Princeton and interim director of the Center for Information Technology Policy at the Woodrow Wilson School of Public and International Affairs. "These results show us that machine learning isn't magic; there are clearly other factors at play when it comes to predicting the life course."

The study [PDF], which was published this week in the journal Proceedings of the National Academy of Sciences, is the fruit of the Fragile Families Challenge, a multi-year collaboration that sought to recruit researchers to complete a predictive task by predicting the same outcomes using the same data. Over 457 groups applied, of which 160 were selected to participate, and their predictions were evaluated with an error metric that assessed their ability to predict held-out data (i.e., data held by the organizer and not available to the participants). The Challenge was an outgrowth of the Fragile Families Study (formerly Fragile Families and Child Wellbeing Study) based at Princeton, Columbia University, and the University of Michigan, which has been studying a cohort of about 5,000 children born in 20 large American cities between 1998 and 2000.
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Researchers find AI is Bad at Predicting GPA, Grit, Eviction, Job Training, Layoffs, and Material Hardship More

Researchers find AI is Bad at Predicting GPA, Grit, Eviction, Job Training, Layoffs, and Material Hardship

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  • Not very good at predicting when I want a pizza.

  • lack of data (Score:4, Insightful)

    by drummerboybac ( 1003077 ) on Wednesday April 01, 2020 @10:42AM (#59897118)
    13,000 data points doesn't seem nearly enough to properly train a model this complex,

    • Re: (Score:3, Interesting)

      by Mr. Dollar Ton ( 5495648 )

      So, "artificial" "intelligence" presupposes a data set equal in size to the population?

      • Maybe not the population, but larger than this. Training a deep neural network requires a large dataset to learn how to accurately make predictions. Thatâ(TM)s how It works. You show it enough pictures of dogs labeled as such, for example, and after training on those images is able to recognize an image of a dog that it has never seen before.

        • Maybe not the population, but larger than this.

          That was a rhetorical question. "Training a deep neural network" is just building a database of dumb statistical rules. They work in situations in which your statistical model is very simple and very clear and there is no need for real analysis and understanding.

          and after training on those images is able to recognize an image of a dog that it has never seen before.

          This is so precisely because recognizing a physical shape is fairly simple problem and can be done with a glorified statistical package. The things mentioned in the summary have nothing to do with that, so they will fail even with a dataset twice th

          • Re: (Score:1)

            by Shaitan ( 22585 )

            The things mentioned in the summary could be reasonably modeled with a simple rules based classification scheme and therefore could be modeled with AI.

            The dirty secret is they undoubtedly adjusted their models to meet with diversity assumptions which is where they deter from actual human evaluation of the same and break. That said, even human level intelligence only does so well at predict the future behavior of other humans in a highly chaotic world. This breaks because whatever correlations underlay stere

            • The things mentioned in the summary could be reasonably modeled with a simple rules based classification scheme and therefore could be modeled with AI.

              Sure, Jan, modelling complex social behavior is so easy, that it will work 100% correctly if only your subtly oozing biases are applied.

            • Re: (Score:2)

              by suutar ( 1860506 )

              They could perhaps be modeled in the aggregate - what percentage of students will have a given GPA, etc. For an individual student it's essentially trying to predict all their interactions with their graders, and for that you need (a) to decide that the universe is deterministic and free will is an illusion, and (b) really good position/velocity figures for the elementary particles in (at least) the solar system, so you can predict neuron firings.

              • Re: (Score:2)

                by Shaitan ( 22585 )

                "They could perhaps be modeled in the aggregate"

                Exactly. This is my point about stereotypes, whatever correlation underlies them tends to hold in aggregate but fall apart when applied to individuals. Stereotypes are essentially the aggregate patterns found by large numbers of actual neutral nets examining large populations. If you correct them away you've likely corrected out the most significant statistical correlations available. Not that I'm claiming they are strong correlations or accurate predictors, t

      • Why not? It is a data set it learns from as opposed to its prediction. It makes sense the bigger the data set the better. What is wrong with a bigger data set?

        • What is wrong with a bigger data set?

          The thinking that a bigger data set will solve your real problem is wrong.

          There are some things (1) that you can reasonably model with dumb statistics. These are either things that have simple relationships that are hard to calculate because there are many, or things you've analyzed creatively, understand well analytically, and can explain why a certain statistical approach would work - that is, why fitting coefficients will produce predictions with a small error.

          Then there are things (2) for which you need

    • Or perhaps rather missing a crucial state variable in the data set?

    • As I recall, Tensorflow needs 70,000 points to recognize shoes.

      But a lot of life turns on chance and interactions with other people.

      The race is not to the swift or the battle to the strong, nor does food come to the wise or wealth to the brilliant or favor to the learned; but time and chance happen to them all.

    • Really? Because most people who have to make these predictions make them based on their experience with lesser populations. And presumably they make their predictions with a higher rate of accuracy. Intelligence is demonstrated by judgement. Judgement is ability to make correct conclusions when faced with imperfect information.
      • Well, no. Nobody makes predictions like these solely on the basis of their first-hand personal experience. It's really hard to quantify what the inputs to their priors actually were.

        In fact, they were only marginally better than linear regression and logistic regression, which don't rely on any form of machine learning...

        "the simple benchmark model with only a few predictors was only slightly worse than the most accurate submission, and it actually outperformed many of the submissions"

        Actually handing

    • Re: (Score:3)

      by mbkennel ( 97636 )

      That's a very large set in social science and the methods should be able to handle it.

      The predictors ranged from very simple to very complex (with strong regularization of course)---the complex ones didn't do that much better than the simple ones. A baseline set of linear and logistic regressions was competitive with the best of the machine learning methods, and all were lousy. That's a consequence of low intrinsic predictability, or if you think about it another way, that the observed outcome has a large a

    • Statistics cannot solve every problem. These things that is reported here is a problem for even professionals to figure out.

      Those who work in hiring probably had hired someone who you first thought would be a really good fit, only to have them being a problem employee.
      The problem isn't data points, but the fact people will give faulty data.
      College GPA: That 2.5 in college may become a 3.5 GPA because after highschool the person no longer has to take classes they really don't want to take, or with the profe

    • They should try it with 10X that number, then 100X that number.
      If there is no significant difference, you know your modelling technique is crap.

  • AI? (Score:1, Troll)

    by RotateLeftByte ( 797477 )

    Oh... silly me, you mean Actual Idiots as in the people who believe this smelly dog poo.

  • Individuals (Score:3)

    by Archangel Michael ( 180766 ) on Wednesday April 01, 2020 @10:50AM (#59897150) Journal

    We keep trying to group people into nice neat categories so that we can exploit them and discriminate.

    The real solution, is understanding that the individual is the smallest group set, and is unique. Any categorization that doesn't have that is going to fail.

    Group modeling is good for getting group statistics. AI can't account for individual outliers that defy standard categorizations.

    • While, sure, AI can't account for individual outliers well, neither can people, when it comes down to it, and in a lot of cases, automated rules can actually increase accuracy, thus the use of checklists and such improving outcomes in flight and surgery.

      Note, the models they were trying to build don't have to be perfect, just good enough to enable authorities to better identify people more at risk and therefore in need of intervention.

      Hopefully there'd be multiple layers of this, so even if somebody turns o

      • While, sure, AI can't account for individual outliers well, neither can people, when it comes down to it, and in a lot of cases, automated rules can actually increase accuracy, thus the use of checklists and such improving outcomes in flight and surgery.

        Note, the models they were trying to build don't have to be perfect, just good enough to enable authorities to better identify people more at risk and therefore in need of intervention.

        Hopefully there'd be multiple layers of this, so even if somebody turns out to actually need help who didn't get it, they'd slide a bit further, more indicators would pop, then they'd get helped. We can only do the best we can.

        If the AI models aren't able to be good predictors, to me that indicates that they aren't looking at the right things, there's something missing, assuming it can be predicted at all.

        The problem, as I see it, is people often place far more confidence in AI predictions simply because it is done by a computer, and then assumed to be unbiased and accurate. As with many models, it can provide some insight as to where to look, but one shouldn't assume that it has identified a set that includes all subjects of interest.

      • Note, the models they were trying to build don't have to be perfect, just good enough to enable authorities to better identify people more at risk and therefore in need of intervention.

        We might be making the mistake of trying to use group statistics to identify individuals. IT CANNOT BE DONE that way. Why? Because there are always outliers who defy the group dynamics. And now, we've mis-categorized someone because AI modeling says they ought to be categorized that way. And now, we've possibly placed a not-so-veiled bigoted stigma upon people needlessly.

        We are all individuals, and that ought to be our starting point, not the ending point. It is way more work to judge people for themselves

    • So sayeth Harry Seldon.
  • Researchers find training an AI with shitty data leads to shitty results. Garbage In, Garbage Out.
    • If it's GIGO, it's not intelligence.
      • If you teach a human garbage information, such as creationism and flat Earth stupidity, they will provide garbage conclusions as well. Are you saying humans are intelligences?

  • Wrong Data, not 'too little data' (Score:3)

    by gurps_npc ( 621217 ) on Wednesday April 01, 2020 @10:57AM (#59897172) Homepage

    Trying to decide people's GPA based on their Chlorestal level will not work, no matter how much data you have.

    Science works like this: a) hypothesize that X will predict Y
    b) Test. c) Conclude d) repeat.

    They tested and found their hypothesis false. That does NOT mean that all other hypothesis are false. It just means they have to pick different kind of data to feed into their AI.

    Or what they call AI is really just a d20.

    • Trying to decide people's GPA based on their Chlorestal level will not work, no matter how much data you have

      Actually you sort of can, and therein lies the problem with a huge amount of these things.

      The reason you can is that GPA and cholesterol are probably both correlated with some underlying factor, e.g. household income. However, GPA is probably conditionally independent of cholesterol given the household income. If you build a classifier, you will probably do considerably better than random, if that co

      • Unless you're excluding the better underlying predictors from the data anything properly developed (maybe that's asking a lot) shouldn't get tripped up by anything that easily identifiable. We already have statistical techniques that allow us to identify these. If you have all of the data points even a simple linear regression should show that some variable doesn't do a good job of explaining the data. Feeding bad or incomplete data to an algorithm isn't going to lead to good results. The acronym/phrase GIG

        • Unless you're excluding the better underlying predictors from the data anything properly developed (maybe that's asking a lot) shouldn't get tripped up by anything that easily identifiable.

          You'd hope so, but only in a perfect world.

          We already have statistical techniques that allow us to identify these.

          Only in some cases, and only if you have the things well labelled. In this case you're probably not trying to predict GPA from a single variable but from the entire collection. And none of the variables (even

      • The reason you can is that GPA and cholesterol are probably both correlated with some underlying factor

        Prove that claim. The people, I am not going to call them scientists because of the quality of their work, from this article tested there hypothesis and it failed because it was stupid to begin with.

        • Prove that claim.

          Wut. OK, that's getting pretty aggressive for a fairly informal discussion. It was drifting towards the hypothetical, but you can often predict all sorts of stuff, but that doesn't make it *useful*.

          Anyway, the article is paywalled. The summary has gems such as:

          In the end, even the best of the over 3,000 models submitted â" which often used complex AI methods and had access to thousands of predictor variables â" werenâ(TM)t spot on. In fact, they were only marginally better t

    • Unless you have a large number of students who are eating cheeseburgers rather than studying.

  • We do not have artificial intelligence. We have big, weighted decision trees that are highly deterministic. AI doesn't exist.

    • We have big, weighted decision trees that are highly deterministic.

      Mostly these days people use deep neural networks rather than random forests.

    • We have big, weighted decision trees that are highly deterministic.

      Those are called Expert Systems. What are discussed here are statistical classifiers (neural networks, etc), which basically divide things into groups in a large multidimensional space.

  • Comment removed (Score:5, Insightful)

    by account_deleted ( 4530225 ) on Wednesday April 01, 2020 @11:00AM (#59897180)
    Comment removed based on user account deletion

    • "no one has yet come up with a reliable way to "win" the stock market"

      The only way to win is not to play.
      All gambling works like that.
      Only the house comes out ahead.

    • I'd like to point out, in support of your argument, that people 'ascribing an almost mythic aura to (so-called) AI' is by far not a new thing; there's at least 100 years worth of fiction and media that present a fantasy image of robots and androids as posessing human-level intelligence, emotions, self-awareness, and sentience; meanwhile you can't even build and program a robot to do something as simple as fold a shirt properly, they spent decades just getting one to walk on two legs, and despite the thousan

    • The simple gritty truth is that it can only do things that people can already do, just faster.

      Not really. AI can do some things that people can't do, and people can do many things that AI can't do. And AI can do stuff faster. As time goes on we'll push back the limits of what AI can do and increase the space of things it can do that people can't, and decrease the space of things it can't do that people can. But it seems likely to be a slow process, and one filled with surprises in both directions.

  • Was the "AI" supposed to find meaningful patterns in statistical noise? Are they trying to prove GIGO is really a thing?

  • The findings of the root study, the FFS (a summary is at: https://www.nichd.nih.gov/news... [nih.gov]) are not surprising, and is stuff most social workers deal with every day. What the goal of the AI model was is unclear. Even reality shows like Live PD pretty much can predict where the crime is, live and direct, every weekend. Too much can not be predicted in individual cases, mainly due to the human factors that no AI can accurately project. It's no true improvement on the human condition, and is a disservice
  • The IQ of the biological parent is well-known by psychometric researchers to be the best known predictor of life outcomes. Yet the paper does not test this. In fact it does not even mention IQ(g) anywhere. This is bizarre. The researchers obviously have no clue what they are doing and have wasted millions in funding. See for instance www.ncbi.nlm.nih.gov/pmc/articles/PMC4170778/ which is only one of hundreds of studies going back 50 years.
  • This study messes with the basis of most liberal ideas. The idea that you can’t succeed and it’s not your fault because of... race... economic status... sex... genetics... or whatever. This basic idea naturally sprouts into misguided ways to “level the playing field”. All these factors don’t have a strong correlation to what happens to you. You determine your life! Life doesn’t determine you.

    • Don't need AI to predict economic success. The most reliable predictor is the economic status of the parents.

      • If you remove that, the next predictor is the parent's attitude towards education and after that is number of books in the home.
  • It relies on surveys and answers that are subjective. A psych study claimed women give wildly different answers to if they have had an abortion depending how you ask. They straight asked and got well below real numbers. Preambled with possible medical complications and it jumped to well above real numbers. They could not win.
  • The first sign is

    co-lead author Matt Salganik, a professor of sociology

    The next sign is

    Fragile Families Challenge
    ...
    Challenge was an outgrowth of the Fragile Families Study (formerly Fragile Families and Child Wellbeing Study)

    And this sign of selection bias

    It’s designed to oversample births to unmarried couples in those cities,

    Now, for the pièce de résistance:

    “Either luck plays a major role in people’s lives, or our theories as social scientists are missing some important variable,” added McLanahan. “It’s too early at this point to know for sure.”

    Please note how this conclusion says either chance has a major role OR "social scientists are missing some important variable".

    It isn't the AIs, it is the assumptions of social scientists ( see the first sign above ) that are the problem.

  • AI's gotten pretty good at some things, but AI is not good at tasks that require a lot of seemingly unrelated information. If humans are bad at a task because it's computationally complex, AIs are going to suck at that task as well. If humans are bad at a task because it's a lot of boring pattern matching over and over again, then AIs will likely be good at it. Looking for video frames in video is pretty easy these days. Accurately predicting movements in the stock market is not.

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