[HN Gopher] AI Explorables: big ideas in machine learning, simpl...
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AI Explorables: big ideas in machine learning, simply explained
Author : smitop
Score : 90 points
Date : 2021-07-05 16:41 UTC (6 hours ago)
(HTM) web link (pair.withgoogle.com)
(TXT) w3m dump (pair.withgoogle.com)
| [deleted]
| biasedbrain wrote:
| These are now the "big ideas" in machine learning? I guess they
| have given up on AGI then?
|
| In my book, this AI diversity nonsense is not even AI research.
| The SJWs pretend they discovered the concept of bias, when it has
| always been a core part of Machine Learning from the beginning.
|
| Even more concerning, it is an attempt to permanently encode
| their distorted sense of reality in AI models that affect
| billions of people. This needs to be fought, not celebrated.
| throwaway789257 wrote:
| I clicked expected to see big ML ideas explained, and all I found
| was Google scrubbing its AI ethics reputation after the debacle
| last year. I hope they devote their energies to actually
| explaining some ideas about ML rather than nattering on about
| fairness.
| Graffur wrote:
| In short: garbage in, garbage out
| azinman2 wrote:
| That's actually not a good summary at all. You could have
| quality in, but apply to a different group than was trained on,
| and get garbage out (eg face recognition trained on one set of
| races or genders, then try to apply to different races to
| genders). Or you train on data that's high quality but is the
| result of inequity (eg police profiling, or credit worthiness),
| and then you get inequity out of the model.
| sharikous wrote:
| God know how nice explanations of ideas in ML would be useful.
|
| But this is ML enforcement of made up "social values", like
| diversity and fairness
| daenz wrote:
| Nobody is actually smarter or better than anyone at anything.
| It's a result of biases and privilege. And if all biases were
| taken into consideration, everyone would (and should) score
| exactly the same on everything, regardless of age, sex, race,
| gender, ethnicity, orientation, etc etc.
|
| That's the underlying message about ML initiatives that touch
| sensitive topics. Yes, we should be very careful not to encode
| biases into our models, and these biases are definitely real.
| Absolutely 100%! But what happens _if_ the models still show
| disparity? Are we prepared for that possibility? Does it mean
| there are more biases yet to uncover and we must root them out?
| Or should we hide anything that reveals disparity in the absence
| of biases? Or are all biases impossible to remove, so they must
| forever remain the explanation for all disparity? Is disparity a
| proof of bias? Because if that is true, then the only conclusion
| is that every person is exactly functionally equal and the only
| differences are human biases.
|
| I've seen smart tech people unable to say that men are on average
| taller than women because (I believe) they were afraid to say
| that some "favorable" outcome (tall) was a result of some
| component that could be genetic. Their argument was anecdotes of
| tall women, and saying there is no such thing as an "average" man
| or "average" woman.
| spoonjim wrote:
| Yes, it's a common "argumentation" strategy on Twitter. "Do you
| know that someone was once born with a horse's head? Clearly
| the concept of 'human' does not exist!"
| mustafa_pasi wrote:
| That is not my understanding of the ethical dilemma. People are
| different, and belonging to groups is highly predictive, and
| that is what ML algorithms depend upon to classify you (every
| feature is a group). The ethical dilemma is, should you be
| doomed to be assessed based on your belonging to an arbitrary
| set of groups? It is highly predictive, but also by definition,
| discriminatory.
|
| Business entities benefit, because on average the predictions
| are right, but individuals are being discriminated against, and
| btw, besides the protected categories, other types of
| discrimination are also unfair.
| biasedbrain wrote:
| You should be allowed to think and perceive and notice
| things, and use tools to help you do so. It is ridiculous to
| mandate algorithms should be modified to hide statistical
| facts about the world.
|
| by all means, educate people that just because some
| population is perhaps on average more criminal, it doesn't
| imply any individual belonging to that population is also
| more criminal.
|
| To hide the statistics would also hurt efforts to find
| solutions. That can not be a good thing.
|
| Edit: if you don't like the criminal example, what about
| noticing some populations are on average poorer than others?
| Would that be a good thing, because government money could be
| diverted their way, or a bad thing, because banks would be
| less inclined to lend them money, and landlords would be less
| likely to rent them a home?
| j7ake wrote:
| It would be nice to do a well-designed experiment to ask what
| are the biological differences in different categories of
| humans (eg old vs young). Then use that ground truth to assess
| whether an ML model is generating the biases that deviate from
| the ground truth. That way there is no a priori assumption of
| what is technical bias and what is real signal.
|
| With the level of investment people are putting into ML
| technologies, these types of ground truth calibrations would
| cost only a fraction of the total budget.
| phreeza wrote:
| > And if all biases were taken into consideration, everyone
| would (and should) score exactly the same on everything,
| regardless of age, sex, race, gender, ethnicity, orientation,
| etc etc.
|
| "Should" is the operational word here. It is a normative
| decision that these (protected in many jurisdictions) classes
| should not receive any different treatment due to membership in
| the class. I agree with this goal, it seems you do not? Or are
| you objecting to a normative decision being disguised as a
| descriptive one? In that case I think it is largely a straw
| man, it's not how most people actually think.
| bordercases wrote:
| > In that case I think it is largely a straw man, it's not
| how most people actually think.
|
| Why do you believe this? Even the notion of bias, which you
| must conceive as real for your claim to hold any weight, is a
| version of the descriptive being confused for the normative.
| And for biases to be as problematic as they are considered,
| they must be common in key places. Why couldn't researchers
| have an unreasonable bias against reasonable biases?
| joe_the_user wrote:
| _Nobody is actually smarter or better than anyone at anything.
| It 's a result of biases and privilege._
|
| Where is the linked article claiming this? Your post seems like
| complete derail of the article discussion for the purposes of
| making broader political points. [insert hn caveat about being
| more nuanced in potentially more flamebait topic]
| nxpnsv wrote:
| The political debate is endless and not very enlightening.
| However, this stuff max constitute useful tools to make better
| models in a non political way. A model that is biased because
| of too narrow test data will perform worse than a model trained
| on a suitable dataset. It can be hard to spot these issues, and
| having some checks and bounds to avoid common sources of bias
| is a good thing. Furthermore, an analysis might expose more
| info about the users than intended. This can have pretty bad
| consequences. For this purpose I thought the one about
| "collecting sensitive information" was a pretty good start.
| NicoJuicy wrote:
| I usually mention that nature experiments more with men than
| with woman.
|
| That's an explanation for why top talent are usually men, but
| the lesser ones are usually men too.
|
| ( + I was it was an explanation of a study)
| 3grdlurker wrote:
| > But what happens if the models still show disparity?
|
| You have yet to qualify exactly what you mean by disparity here
| --it doesn't help that the example you used (i.e gender-height
| correlation) isn't an ML model.
| Jack000 wrote:
| It shouldn't be controversial to say that on average, men are
| taller than women. However it is a problem to say that "since
| you are a woman, I infer that you must be short". The
| difference is (not really) subtle but the first statement can
| be true while the latter statement is obviously fallacious.
|
| With an objective quantity like height it's easy to see the
| error, but harder with less well-defined quantities like
| intelligence, character, etc..
|
| It's frustrating to see people talk past each other with ill-
| defined terms.
| daenz wrote:
| >It shouldn't be controversial to say that on average, men
| are taller than women. However it is a problem to say that
| "since you are a woman, I infer that you must be short". The
| difference is (not really) subtle but the first statement can
| be true while the latter statement is obviously fallacious.
|
| I agree, but the person I was speaking about specifically was
| the former, not the latter. They were extremely resistant to
| the idea of measurable biological differences. This is an
| intelligent, science-believing person.
|
| I think there are a few main groups that people fall into
| around these kinds of subjects:
|
| 1) People who suspect that there may be differences in groups
| of people, but know that acknowledging them will probably
| lead to arguments that support sexism, racism, homophobia,
| transphobia, etc (because they have historically), and so
| they avoid acknowledging any differences that can't be
| explained by bias.
|
| 2) People who _want_ there to be differences in people so
| that they can justify their biases against groups of people.
|
| 3) People who _don 't want_ there to be differences in
| people, because it doesn't align with a fair worldview that
| they want to be real.
|
| 4) Everyone else who just want people to be honest about
| potentially uncomfortable truths.
| jimmygrapes wrote:
| Where are all the #4s?
| [deleted]
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