[HN Gopher] 'Simple' AI can anticipate bank managers' loan decis...
___________________________________________________________________
'Simple' AI can anticipate bank managers' loan decisions to over
95% accuracy
Author : Hard_Space
Score : 89 points
Date : 2022-02-19 05:49 UTC (17 hours ago)
(HTM) web link (www.unite.ai)
(TXT) w3m dump (www.unite.ai)
| throwaway81523 wrote:
| How does "simple AI" compare with an old fashioned small table
| lookup and a few "if" statements?
| google234123 wrote:
| https://en.wikipedia.org/wiki/Random_forest
| ninjinxo wrote:
| https://en.wikipedia.org/wiki/Expert_system
| DrRavenstein wrote:
| Difference is that random decision forests learn the rules
| for themselves, they don't have to be programmed in
| manually by experts. They're one of the most performant
| "pre-deep-learning" machine learning models and a sensible
| baseline for many ML tasks before you go out and buy a
| $1500 GPU
| Closi wrote:
| The ability to program approval rules and understand why
| decisions have been made (and then tweak the ruleset
| based on economic/statistical analysis) is a feature for
| these sort of organizations, not a limitation.
|
| There will be elements of AI which are useful, but
| ultimately banks will want to know _why_ a certain
| decision was made, and want to incorporate their own
| economic calculations and forecasts into the model.
| visarga wrote:
| Decision forests don't provide that explainability
| though, how can you interpret averages of hundreds of
| decision trees?
| bob1029 wrote:
| In my experience, many financial products marketed as having
| "intelligence" are indeed just layers of SQL queries (rules).
|
| The benefit of using SQL rules engine in a financial setting is
| that you can prove causality and intent throughout. Why a
| customer was declined for a loan can always be traced
| deterministically to some rule SQL that legal previously
| approved per regulations in that jurisdiction.
| woadwarrior01 wrote:
| Decision trees (the base learner in random forests and gradient
| boosted decision trees) are effectively nested "if" statements,
| and predictions from tree ensembles (RF and GBDT) are
| effectively averages of predictions from those nested "if"
| statements.
| hooande wrote:
| This isn't surprising. Most 'AI' comes down to correlating a
| small number of variables (one or two) with a prediction target.
| The real benefit of any form of machine learning is detecting
| functional relationships between variables (ie, "when this AND
| NOT that OR this"). It's just that these relationships don't
| provide a real benefit in 99/100 real world use cases.
|
| In the case of a loan, if the credit score is high they'll
| probably qualify. If it's low, they won't. All other variables
| are very minor in comparison to this one, which already
| encapsulates almost all of the relevant information. This is by
| far the most common situation for practical machine learning.
| roenxi wrote:
| Yes. But there is also an interesting failure mode - people
| don't bother applying if they don't think they'll get accepted.
| So presumably even just a "YES" will be a fairly good model of
| what actually happens without necessarily capturing the real
| model that the bank is using. A 95% accuracy rate isn't
| necessarily very good because of this effect (it might be, it
| just isn't certain).
| sdoering wrote:
| My SO used to work in a bank in the business loan securities
| back office. Believe me. Lots of people and businesses apply
| even if they themselves see no realistic chance of receiving
| a loan.
|
| And in private non business customers there are even a
| significant amount that have less than nothing to their name,
| living of welfare and still don't see, why they should not
| receive a loan from the bank for the newest iPhone. Or a new
| car. Or TV because "football's coming home".
|
| Excuse my snarkiness, but the stories she told me from her
| apprenticeship in the bank, when she was customer facing were
| not painting a good picture of humans (Btw. well off or not).
| But that is probably the case in any consumer facing job.
| jacquesm wrote:
| Excuse _my_ snarkiness, but having actually worked for a
| bank and seeing the inside of the loan department from an
| IT perspective, the bulk of that translates into: banks
| will allow the people that are already wealthy to leverage
| to make them even more wealthy while working hard to keep
| those already down firmly in their place. Not to mention to
| take advantage of them by charging them exorbitant fees for
| the little bit of credit that is extended to them.
| User23 wrote:
| Since you have industry experience how would you have
| banks rectify this perceived injustice? I can't conceive
| of an underwriting process worthy of the name that isn't
| going to consider those borrowers with collateral more
| creditworthy than those without.
| djbusby wrote:
| For one, overdraft fees should only be charged once per
| calendar month, not for every overage.
|
| Also, the burden on the bank for an overdraft is near
| zero, so why is it $35 for the customer? Perhaps a fee of
| only $5 would be less regressive?
| User23 wrote:
| I don't know if it's still the case, but it used to be at
| least some US banks would order pending transactions in
| descending order of amount to maximize overdraft fees.
| For example, if you had $100 in your account and had
| charges for $1, $1, $1, $1, and $100 then as a
| "courtesy"[1] the bank would clear the $100 and then
| charge $35 times 4 for the remaining transactions.
|
| Anyhow that's really awful and needs to stop, but I don't
| see eliminating overdraft fees making a real dent in the
| advantages wealthy borrowers have.
|
| How could underwriting be changed to identify truly
| creditworthy borrowers who don't have any financial or
| real assets? We already know that simply lowering
| underwriting standards has problems.
|
| [1] Their words, not mine.
| skybrian wrote:
| Yeah, that's a very high-interest loan.
| sdoering wrote:
| I am with you. Shitty entitled customers calling female
| tellers whores that need to be f**ed to loosen up a bit
| (happened more than once) do not absolve banks from their
| abysmal policies. Or the system that not only enables but
| sometimes regulates/necessitates these policies.
|
| I am not siding with banks in general here. I was just
| exemplifying one aspect to show how it is not always
| black and white.amd how there can be unreasonable
| customers.
|
| [Edit typos]
| mvc wrote:
| > why they should not receive a loan from the bank for the
| newest iPhone
|
| Excuse _my_ snarkiness but as someone who 's been poor and
| needed a phone, I'd have got a loan to buy a 2nd hand phone
| if anyone would've given me one. Trouble is, both the
| companies providing the phones, and those providing the
| loans seem to prefer that I get the brand new one.
| sdoering wrote:
| I am not talking about reasonable needs here. I am
| talking about people who are Cleary feeling entitled to
| receive loans they know the can never repair and would
| not even be willing to try.
|
| I myself had to live on 345 Euros a month for quite some
| time. And even longer was living below what Germany
| considers the amount of monthly income that one would
| qualify to not be called poor ("Armutsgrenze"). I know
| the feeling and hear you. The only difference is that I
| could not even get a contract as no telco would find me
| creditworthy enough. Needed to do prepaid and see how I
| would get any old mobile.
| kqr wrote:
| Yup. This has been shown over and over by Meehl and that gang.
|
| > The real benefit of any form of machine learning is detecting
| functional relationships between variables (ie, "when this AND
| NOT that OR this"). It's just that these relationships don't
| provide a real benefit in 99/100 real world use cases.
|
| And the real problem with humans, ironically enough, is that
| due to narrative fallacy, confirmation bias, etc., humans
| vastly overvalue the contributions of these special
| circumstances.
|
| If a dumb 2--3 variable rule predicts your friend will go to
| the movies in the weekend, you are likely to go, "Yeah, maybe.
| But she complained of that headache this morning, maybe there's
| something deeper there that also makes them not want to go to
| the movies."
|
| In most cases, you'll be wrong to override the dumb rule.
|
| So why do people do this? Well, the original movie-going
| prediction might only have been a 55 % shot. So 45 % of the
| time, your friend won't go to the movies, headache or not. But
| if they don't, you'll think you made the right call considering
| that headache.
|
| If they do go, you'll end up thinking, "Right, of course. They
| had the headache but their friend really wanted to go so of
| course they would endure it."
|
| In other words, you'll think of a way to frame your incorrect
| call as the right one. (Hindsight bias.)
| sdoering wrote:
| When dealing with stakeholders in big corporations I learned
| that they absolutely needed AI, machine learning and data
| science as capabilities in the projects that they started. Even
| if data was only very tangentially related to the core
| product/project. If so, for example one needed to datascience
| the shit out of project metadata to create a project efficiency
| self optimization loop. Or what ever crap one could come up
| with.
|
| Else they would not be able to secure funding for the idea from
| upper management because they had learned that 'data is the new
| oil'.
|
| The second VS part always needed/needs to be how this project
| enables the product to be (or become) a platform where the
| company can the run value added services on top (value adding
| for the company, not the customer).
|
| Imagine a car being a platform and you can book additional
| horse power for the upcoming trip. Or change the background
| image for the instrument panel. Or pay extra to natively have
| access to your Spotify playlists. And then datascience the user
| data to recommend even more stuff resulting in $$$ (by
| imagination from management and fuelled by slide decks created
| by some junior strategists that have never even owned a car but
| presented by the client account lead in a lush retreat).
| IgorPartola wrote:
| I am unclear as to whether what you are saying is that they
| need data science to justify their projects because when they
| say that an AI told them that they need $X extra for the
| project it carries more weight than when they say they did
| the analysis by hand or if the data science actually provided
| value in this case.
| sdoering wrote:
| No. They need AI in their projects to receive management
| buy in (and funding) because management swallowed the hook
| and now wants everything AI & data driven (even if it makes
| no sense) to be able to brag to other managers from the
| competition and other industries that they now use AI and
| are a data driven company.
| DrRavenstein wrote:
| This headline and article are horrible and misrepresent the
| problem and the outcome.
|
| The paper is about the manual process of re-assigning a credit
| score on a scale of 1 to 15 based on other customer criteria.
| Really the fact that this process exists at all shows that their
| initial credit scoring approach is flawed or too simplistic. The
| argument of "just replace it with an if statement" does not hold
| up in this scenario.
|
| So this is not a "if number big lend. If number small no lend"
| problem. Its a 15 way multi-class classification problem. They
| even give a baseline for what happens if they randomly pick or
| always pick the biggest class in the paper
|
| > As is typical in machine learning we also report the Accuracy
| p-value computed from a one-sided test (Kuhn et al., 2008) which
| compares the prediction accuracy to the "no information rate",
| which is the largest class percentage in the data (23.85%).
|
| So yeah, 95% is somewhat better than 23.85%.
|
| I agree with the general sentiment that is is likely a fairly
| straight forward problem to predict if you are familiar with the
| bank's operating procedures as there is no way these individuals
| are making their own risk models and independent decisions. They
| are there to follow the rules and provide human accountability.
|
| An error analysis on the items the model couldn't predict would
| definitely have been most interesting.
| edmundsauto wrote:
| Plus, systemic risk of a repeatable exploitation is more likely
| without humans in the loop. Making a bad loan for $1M is bad,
| but if "attackers" can repeatedly prove until they get a bad
| risk $1B loan, it becomes business shattering.
| visarga wrote:
| Do they remove the human in the loop? That doesn't seem like
| a smart idea. A model would be good just for suggestions.
|
| Humans are both biased and with high variance (not to mention
| corruptible), but the algorithm can benefit from much better
| scrutiny and ensure uniform application of the criteria. If a
| human overrides, then they got to have a good reason.
| jakey_bakey wrote:
| "Heuristics that work 95% of the time"
| throwthere wrote:
| I mean Zillow's valuation platform that bought homes had great
| accuracy. We all know how that turned out-- Zillow ended up
| buying a whole bunch of houses for much more than they were
| worth. Such as the fate of recommender systems operating an
| auction markets with humans who know better.
| jokethrowaway wrote:
| I really wish we would stop calling this AI.
|
| I understand startups need funding from VCs whose idea of
| technology is "BLOCKCHAIN AI" but, being interested in AI, the
| amount of spam I have to dig through to find actual AI work is
| insane.
| shadowgovt wrote:
| What does actual AI work look like these days?
| anaganisk wrote:
| If key==0: print("its zero") else: print("here is your 10 sec
| un-skippable ad")
| forgingahead wrote:
| minimum_credit_score = Loan.joins(:borrower).where("loans.default
| = true").average("borrowers.credit_score")
|
| if loan_applicant.credit_score > minimum_credit_score
| decision = "approve"
|
| else decision = "reject"
|
| end
| zwaps wrote:
| Would a linear decision barrier not be fit by the logistics
| model?
| ant6n wrote:
| You want to use the average credit scores of all defaulting
| loans as threshold? That seems really low, you're setting your
| bank up for a lot of defaults. But then there's a lot of
| selection bias in your data -- presumably your bank has been
| denying loans to people with bad scores, so over time you your
| minimum credit score is the upward inching average inside the
| cracks between safe and denied loads.
| sokoloff wrote:
| It depends heavily on the interest rate as well. If the
| interest rate is high enough, you can show a profit even with
| a fair number of loans sent to collections.
| lordnacho wrote:
| I had a look at LendingClub data and indeed unsexy RF and GB were
| pretty good. This shouldn't surprise us though, because what are
| the likely predictive features other than how much the person
| makes compared to what they want to borrow? Sure, there's what
| they intend to do with it, perhaps adjustments as well for how
| high their income is relative to where they live. All things that
| a model can help quantify, but nothing terribly surprising
| either.
|
| What's left is a bit of judgement as to whether an applicant is
| temporarily showing wrong data, eg if they're a student starting
| their first job, what is their income, low or high? But basically
| it means the bulk of cases can be done by machine and the loan
| officer looks at some corner cases.
|
| Note this person is also an actual person, i.e. capable of being
| held to account. I once worked with a prime broker who decided to
| waive the red flagged checks on a certain client in order to win
| the business. The guy blew up badly, the bank lost money, and he
| lost his job.
| mjburgess wrote:
| Accuracy is almost never the right statistic. Here,
| _profitability_ is. As soon as you realise the target statistic
| applies a human-goal-value-weighting to the prediction outcomes,
| it should be easier to see why ML systems are often unsuited to
| the task they 're set.
|
| Here, those 5% of cases are likely business-ending or business-
| making: precisely because they arent naively routine.
| kqr wrote:
| I think you can be even bolder: accuracy _never_ matters. It 's
| always about the consequences, not probabilities. Sometimes the
| two are the same, but most of the time they are not.
|
| Also worth noting that the arithmetic expectation is only a
| good way to measure the profitability if we are talking about
| small amounts compared to your total wealth. For any other
| case, you should use the geometric expectation of total wealth
| to evaluate options. (This is equivalent to maximising log
| wealth, the Kelly criterion.)
| yobbo wrote:
| Outcome * probability = expectancy, which is the quantity
| usually maximised or minimized in AI.
|
| The problem in this case was defined as reproducing the human
| scores. Mainly, it's a demonstration of the information
| content in the data and the scores. To me, it demonstrates
| how simplistic the human scores are.
|
| With information about historic outcomes, we could have
| compared the effectiveness of the "credit score method" with
| some other AI algorithm that was optimizing total value.
| kqr wrote:
| One little expansion: what you described first is the
| arithmetic expectation, which is a good approximation when
| the numbers involved are small compared to total wealth.
|
| When you start taking larger bets, you want the geometric
| expectation of total wealth, i.e. (current wealth +
| outcome)^probability.
|
| (This is the Kelly criterion for judging significant
| opportunities.)
| lngnmn2 wrote:
| Because this is a trivial problem reducible to an n-dimension
| vector.
| richardfey wrote:
| If you want to impress me, tell me also the results of these:
|
| - Performance against a fair dice - Performance against a group
| of humans trained to predict bank managers' loan decisions
| jdrc wrote:
| i d be more interested if the ai made a better financial
| decision than the managers
| DrRavenstein wrote:
| The article is a bit rubbish. They're not predicting on the
| binary "would we give these people a loan or not" but they're
| predicting manual corrections fo credit scores by bank
| managers. It's a 15 way classification problem (1 is low score,
| 15 is high). The data is distributed in a bell-curve like way
| with the most people in the 6 or 7 bracket.
|
| From the paper:
|
| > As is typical in machine learning we also report the Accuracy
| p-value computed from a one-sided test (Kuhn et al., 2008)
| which compares the prediction accuracy to the "no information
| rate", which is the largest class percentage in the data
| (23.85%).
|
| So fair dice 23.85%, model 95%.
|
| That said I bet a human who had read the banking rules and
| regulations and recommendations on lending could easily match
| this performance.
| Brian_K_White wrote:
| Title is oddly framed. What is interesting or useful about merely
| not-quite predicting what a human will do?
|
| Do the AI's 5% discrepency picks perform better or worse than the
| human's picks?
|
| The title is worded to suggest the AI can, or is very close, do
| the human's job, which could totally be so.
|
| But it could also be that the AI loses 5% vs the human, and the
| bank only makes 5% on loans in the first place, and so losing 5%
| of them is like totally erasing the entire income of the bank
| (from loans) which makes the AI a complete Hindenberg, rather the
| opposite of the implication from the title.
| jsemrau wrote:
| It takes a long while to validate this statistically though as
| default cohorts move through the lending cycle.
| Closi wrote:
| Absolutely! There is not enough data here to signify the actual
| performance of the algorithm.
|
| Without more data it's not clear if the algorithm is actually
| effective - e.g. if the approval rate of these loans is 80% for
| instance, a formula of "always return true" could be said to be
| '80% accurate compared to human decisions'.
|
| And if the AI makes the same decisions as a human 95% of the
| time, but then makes horrific errors the other 5% of the time,
| it wouldn't be an appropriate replacement for the human. This
| is the same issue with self driving - it doesn't matter if you
| make great decisions 99% of the time if 1% of the time you
| decide to drive into a wall (until you have got your error down
| to a place where it's safer on aggregate than a human).
| kqr wrote:
| Given the research by Meehl, if there's a decent numeric rule
| and a human decision that are closely predicting each other, my
| money would be on the numeric rule being slightly more
| accurate.
| citizenpaul wrote:
| If you spend 5 minutes in a bank this is not surprising. Probably
| 99.99999% of most banking loan staff have zero input on if a loan
| is accepted or not. They just follow the rules set from on high
| and tell you yes or no to your face instead of a computer. Their
| job is really just to calm you down when you get rejected or tell
| you what you have to do to get accepted from their secret rules.
|
| I even worked on a loan program years back to centralize a bunch
| of acquired banks. They literally told their staff not to tell
| customers there was nothing they could do and a set of rules in
| the computer made 99% of the decision for the loans now.
| Flip-per wrote:
| As bank I'd look into these 4.x % of loans where the machine
| learning disagrees. This headline feels like 20 years old though,
| except for calling it "AI".
| Ekaros wrote:
| Loan decision is relatively straightforward algorithmic process.
| I see no reason why AI is needed or wouldn't give similar
| answers.
| platers wrote:
| Shouldn't the sentence be flipped in that case? I see no reason
| why expensive human is needed or wouldn't give similar answers.
| toomanydoubts wrote:
| The point is that it can be solved with simple algorithms. No
| expensive human nor AI needed.
| Ekaros wrote:
| Not to forget that AI can be essentially a black or grey
| box. You have some inputs and you have some outputs. Mostly
| correct, but what if that fails catastrophically? At least
| algorithms, in this case can be mostly walked through by
| regular humans and errors possibly be noted. Unless the
| complexity isn't idiotic in charge of profits.
| Ekaros wrote:
| Mostly they are there as sanity check. Just collect the
| numbers and input in the system. Humans really can't be
| trusted, especially with money. So someone in the loop have
| to check can the information provided be trusted.
| Brian_K_White wrote:
| I think they mean algorithmic as in, simpler and
| deterministic and auditable plain if/then/math algorithm, vs
| black box magic AI.
|
| Even if an AI can produce seemingly the same results as a
| human, it should be out of the question anyway to let an
| inscrutable black box make decisions over people's lives.
| Because at least with a human you (their boss, or a judge,
| etc) can ask them "Why did you decide that?" and they can
| tell you. A racist or mysoginist or religious human can be
| identified and fired or corrected etc. How do you judge if an
| AI is giving inhumane decisions?
|
| The decisions themselves can't really be judged, only the
| process that generated them, and you can't see that process
| in an AI.
|
| If someone doesn't get a loan, and someone else does, you
| can't tell that wasn't right just from the final result.
|
| Even if the results "look" wrong, like only 30% of black
| people get the loan while they made up 40% of applicants,
| even that could possibly be exactly correct, but you can't
| know if you can't see the process.
|
| But a human can be asked, and simple algorithm code can be
| read.
|
| Probably these days the human has so little discretion anyway
| that the corporate policy is the algorithm and the human is
| pointless anyway except as a sham human-looking interface to
| appease customers. It helps sales to have a human, but the
| human in fact wields none of the human power that the
| customer wants a human for.
| cosmodisk wrote:
| Those days when you dress up, go to your local bank and the
| banker makes a decision based on their 'knowledge' about you,
| are long long gone. Loan applications are standardised and
| things like credit scores play big role. Things are probably
| a little bit different when we are talking 7-8 digit loans.
| notahacker wrote:
| The absence of human picking up obvious discrepancies the
| model isn't trained on or doesn't accept as inputs is an
| order of magnitude or three less expensive than making more
| bad loans (of the size/rate that aren't already determined by
| a basic credit check). Also, checking the loan matches the
| bank's credit criteria isn't the only task a bank employee
| performs during the course of their employment, which likely
| includes quite a few the AI is utterly terrible at, like
| talking to people.
| Ataraxiaist wrote:
| Every bank is already using a data driven model for credit
| decisions too. I am sure that is the major input for the human
| decision already.
| graycat wrote:
| Some more details would be nice.
|
| So a loan can be good or bad. The loan officer can rate it as
| good or bad. And the software can rate it as good or bad.
|
| To evaluate the situation, would like more than the "95%":
|
| (1) Would like to see the arithmetic that yielded the "95%". (2)
| Would like to know the rate (probability) of _false positives_ ,
| when the software said the loan was good but it wasn't. (3) Would
| like to know the rate (probability) of the _false negatives_ when
| the software said the loan was bad but it was good.
|
| And, really, when the software and the loan officer disagreed,
| what were the _ratings_ of the applications and when was the
| software correct and when, the officer correct.
|
| Finally, what was the average cost per mistake for the false
| positives and for the false negatives. E.g., a false negative
| could cost the bank some business but a false positive could cost
| them $millions.
| epolanski wrote:
| I don't think you need AI. Like in Italy the total of your loans
| cannot exceed 1/3rd of your monthly salary, as simple as that.
| kqr wrote:
| Does this mean most Italians rent their homes for life and a
| small fraction purchase their homes in cash?
| epolanski wrote:
| No, we have some of the highest home ownerships in the world.
|
| 1/3rd of salary is more than enough to buy you a home. That's
| enough to get a 200k loan for 30 years while making average
| salary (1700 net euros).
| kqr wrote:
| Aha you mean the _interest_ cannot exceed 1 /3 of salary,
| not the principal? That makes more sense.
| epolanski wrote:
| I'm not sure I expressed myself correctly: whatever you
| make you can't have your loan payments exceed one third
| of your monthly salary. So if you make 2000 euros, your
| loans cannot exceed 666 EUR.
| sokoloff wrote:
| More likely to be based on the payment (principal plus
| interest) than on the interest alone.
| bencollier49 wrote:
| Genuinely surprised that "bank managers" either exist or have any
| say in loan decisions. In the UK it appears to be pretty much
| entirely computerised, with a small amount of oversight from
| teams of analysts if the computer decision is borderline.
| Different elsewhere?
| xiaodai wrote:
| I reject 95% of loan application, so if u just predict reject
| 100% of the time, then your accuracy is 95%. Just stupid shit.
| fancyfredbot wrote:
| If I run N different machine learning models over the same data,
| and each has some random error in fitting the objective function,
| then I pick the one which matches the validation data best, isn't
| there a danger of picking the one which was "luckiest" with the
| random errors? Presumably for large N that's a real problem? How
| do people account for that?
| mjburgess wrote:
| https://en.wikipedia.org/wiki/Cross-validation_(statistics)
| fancyfredbot wrote:
| Cross validation doesn't solve that problem. As the Wikipedia
| article says: "The variance of F* can be large.[26][27] For
| this reason, if two statistical procedures are compared based
| on the results of cross-validation, the procedure with the
| better estimated performance may not actually be the better
| of the two procedures (i.e. it may not have the better value
| of EF). Some progress has been made on constructing
| confidence intervals around cross-validation estimates,[26]
| but this is considered a difficult problem. "
| mjburgess wrote:
| Well the historical data {(x, y)...} is assumed to be
| distributed according to the true distribution, such that y
| = t(x) where t is the true function which maps x to y. Of
| course, in many situations, no such function exists (ie.,
| there are genuinely ambigious xs, such that t(x) cannot
| produce a single y -- consider an ambigous cat/dog
| picture).
|
| If we sweep models f1,...fn across the validation set ...
| and choose max() of scores() of f1..fn on V, we get f*.
|
| Now if your issue is that f* might be an "unlucky draw",
| you're right. But there is no statistical way of fixing
| this -- if we know (via experiment, etc.) what the true
| distribution is, we can meaasure |f* - t| -- but if we knew
| this, we wouldnt bother finding f*.
|
| If you want a mechanism to mitigate these problems, there
| is one main one: the scientific method. To test whether f*
| poorly reflects t, go and do some experiemnts. If you
| can't, then you wont know.
|
| (Hence: there is no way of doing science via "mere
| statistics". It is the experimental conditions constructed
| by concept-laden, in-the-world, experimenters which are
| able to obtain the sequence of datasets needed to give
| confidence to any given model. ML is therefore not able to
| know anything, its "conclusions" enterily derivative-of,
| and limited-by, human experimentation. The intelligence
| occurs in the experimental design, when that's done,
| everything else is "stamp collecting").
| ssivark wrote:
| https://astralcodexten.substack.com/p/heuristics-that-almost...
| nikanj wrote:
| I was just coming here to post the same link. The 95% accuracy
| should be compared to the general approval rate - does the
| fancy AI beat a simple return true?
| kqr wrote:
| Yup. I always advocate that the first model one should build
| is the one that constantly returns the most common answer.
|
| That way, you can compare the more fancy stuff to something
| to see whether you're really improving.
|
| You also get to evaluate the economic gains of the more
| sophisticated model against the development, maintenance, and
| data costs of it, compared to the dumb one.
|
| (Another good baseline is returning a random historic result
| in proportion to how often it occurs. It sometimes helps
| against exploit attempts at the expense of data.)
| zwaps wrote:
| The logistic regression would likely pick up on that if it
| were a stratification issue.
| lumost wrote:
| I had an uncle who worked on a rural independent regional bank.
| While this was a while ago, the bank could and would use a
| variety of factors including references, propensity towards
| substance issues and others to make a loan.
|
| I wouldn't doubt that if you sat down, and created a data
| set/data collection scheme to gather this data you could make an
| algorithm to closely mirror the outcomes of a loan decision.
| However as a human, the loan officer might simply add a new
| criteria as desired.
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