[HN Gopher] Microsoft's Kate Crawford: 'AI is neither artificial...
___________________________________________________________________
Microsoft's Kate Crawford: 'AI is neither artificial nor
intelligent'
Author : 9wzYQbTYsAIc
Score : 129 points
Date : 2021-06-06 15:07 UTC (7 hours ago)
(HTM) web link (www.theguardian.com)
(TXT) w3m dump (www.theguardian.com)
| rgbrenner wrote:
| _AI is neither artificial nor intelligent. It is made from
| natural resources and it is people who are performing the tasks
| to make the systems appear autonomous._
|
| Making something from natural resources does not make it natural.
| If that were the case, we wouldn't have the word "artificial",
| since everything we make comes from natural elements and
| everything would be "natural". The fact that you took natural
| resources and built something else from it--that it didn't exist
| in nature already--makes it artificial. AI is definitely
| artificial.
| efnx wrote:
| I'm not arguing with your point, but let me offer another
| thought. The crux of this problem as I see it is that people
| consider themselves as separate from nature. The truth is that
| we are part of nature and the things we make are still part of
| nature. The opposite of "natural" is not "artificial" or "man-
| made" - it's "supernatural"!
|
| Of course the word "artificial" is useful to classify things
| for our safety and benefit, but we are not supernatural and so
| the things we create still exist in nature - like we are the
| hand of the universe reconfiguring itself.
|
| This artificial separation of the human from nature has been
| popular in the past and I hope we overcome it.
| pkdpic_y9k wrote:
| That's a nice clean breakdown of that wording issue. I get her
| point though I guess, but I was hoping there might be something
| deeper.
| [deleted]
| aarch64 wrote:
| Kai-Fu Lee and David Siegel talked about AI in April 2019 and
| mentioned:
|
| > It's neither artificial nor intelligent
|
| Quote is at 3:40 (topic starts around 3:05) in the YouTube
| video linked at the end of the article:
|
| https://www.twosigma.com/articles/ai-past-and-future-a-conve...
| ozymandias12 wrote:
| Nice catch! How do you even
| jcims wrote:
| I had the exact same reaction, but I wonder if I'm too close to
| it to appreciate what the term might conjure up in the broader
| (voting) public that isn't in the industry. (FWIW I kind of
| like the term synthetic intelligence as an alternative.)
|
| I do agree wholeheartedly that, much as in the case of
| cryptocurrency, there isn't an intuitive link between the
| product and the resources it requires to create and operate.
| The fact that GPT-3 would cost millions to reproduce in the
| commercial market doesn't even compute for me.
| smoldesu wrote:
| Honestly the hardest part about integrating AI into our daily
| lives is the fact that we don't really have AI yet. We have made
| great advances in the fields of machine learning and neural
| networks, but actually getting a computer to make educated
| decisions is _hard_. The current issue is that all of these
| models are black boxes: an ML model can guess which data will
| come out, but it can 't really fully understand what it knows. It
| can identify slightly harder to notice patterns, but it can't
| actually _think_.
|
| We adopted the phrase "AI" far too early in the field, and I
| suspect there will be another few decades before we have the
| technical and scientific capability to make real artifical
| intelligence a thing.
| otabdeveloper4 wrote:
| > The current issue is that all of these models are black boxes
|
| Not quite. It'd be great if that was the only issue with neural
| network ML. A much bigger issue is that neural networks have
| extremely limited applicability. They're great for
| classification problems when you can have huge training
| datasets, but for many common problems they're useless.
|
| One obvious class of problems is time series prediction -
| extremely important in life and for business, and something
| neural networks are no good for.
| version_five wrote:
| There are many ML time series prediction techniques.
|
| And as a sibling points out, pretraining (not to mention
| various other low data methods) make big NN models useable
| even on small datasets.
| throwaway3699 wrote:
| Doesn't transfer learning level the playing field a bit? We
| already have excellent, off-the-shelf models for NLP,
| computer vision, ODT, etc... that just need fine-tuning for a
| particular business or domain problem. I think the 'huge
| training datasets' requirement is lessening every day.
|
| If you're doing something novel, then sure. I can see that.
| thibautg wrote:
| "Approximate Imitation"?
| rektide wrote:
| James Martin called it true in 2001 when he described the coming
| machine learned systems Alien Intelligences. unintelligible
| inhuman systems. from After the Internet: Alien Intelligence.
| jjcon wrote:
| > AI is neither artificial nor intelligent. It is made from
| natural resources and it is people who are performing the tasks
| to make the systems appear autonomous.
|
| People performing the tasks? As in coding tasks? That's kinda the
| definition of artificial. If AI spawned naturally... that's what
| wouldn't be artificial.
|
| Or is she referring to data labeling and forgetting the many non
| supervised areas of AI? (Not that labeling would make it less
| artificial)
|
| Or is she suggesting that anything made from natural resources is
| natural... is anything not made from natural resources? The
| reason for artificial in artificial intelligence is to juxtapose
| against biological intelligence not speak to resource usage.
|
| I think arguing on the 'not intelligent' front is fine but that's
| just kind of a word game. The field seeks intelligence and is
| fine making stupid ai if it means better ai later. Unless she is
| getting into notions of a soul or some bs about consciousness in
| which case we have left the realm of science altogether.
|
| Any way you slice it seems more like an inane point for making a
| headline than one for substantive discussion.
| grawprog wrote:
| That's because that was an inane point used as a headline that
| didn't really have much to do with what the article is actually
| about.
|
| It's a criticism of the training data sets used on ai's that
| were manually tagged by individuals that's led to some extreme
| biases in ai behaviour resulting in real world consequences.
|
| For a great real world example of this happening right now,
| there's a fairly large scandal and a whole bunch of angry
| people over the problems caused by latitude's choice of
| training material for their ai driven text adventure game.
|
| https://gitgud.io/AuroraPurgatio/aurorapurgatio
| jjcon wrote:
| > that didn't really have much to do with what the article is
| actually about.
|
| That was a quote of the author not just the headline
|
| > It's a criticism of the training data sets used on ai's
| that were manually tagged by individuals
|
| Again though this is just a fraction of AI. Lumping all of AI
| into the realm of 'manually curated supervised learning' is
| just incorrect, yet the author seems to think that's all
| there is, it's either that or they are just trying to make a
| headline.
|
| > For a great real world example of this happening right now
|
| I hardly think a controversy about a random text game on the
| internet warrants such a serious tone
| PoignardAzur wrote:
| Glancing at the table of contents...
|
| _> X. Count Grey: Enslavement and Slaughter of Children_
|
| _> XI. Kyros: Kidnapping, Enslavement, Unethical
| Experimentation, and Torture_
|
| What the hell am I about to read?
| grawprog wrote:
| The training data for Ai Dungeon.
|
| https://play.aidungeon.io/
| tracnar wrote:
| To me she is referring to what she says previously:
|
| > Also, systems might seem automated but when we pull away the
| curtain we see large amounts of low paid labour, everything
| from crowd work categorising data to the never-ending toil of
| shuffling Amazon boxes.
|
| So the artificial and intelligent part is a tiny piece of the
| task, most of it is done through humans and logistics.
| Cybotron5000 wrote:
| It seems to me somewhat similar to an argument put forward by
| Jaron Lanier: that what is termed artificial intelligence's (or
| 'deep learning''s etc.) current successes, at least as far as
| something like the translation services of the big corporations
| for example, is actually built by leveraging corpuses that are
| the fruits of a huge amount of individual human effort.
| Sometimes, say with the 'Mechanical Turk' thing that Amazon
| has, or CAPTCHAs or something, this is a more explicit
| connection (...bit Wizard of Oz! :) As I understand it, he
| proposes that an alternative to UBI etc. might be micro-
| compensations (or transactions) in return for providing this
| data. This might be a prelude/transition to a stage where our
| basic needs (on a sort of Mazlow's hierarchy) were met by A.I.,
| or that there might be increasingly creative or interesting
| ways to complete some tasks that we could go on refining
| forever.
| jjcon wrote:
| > are the fruits of a huge amount of individual efforts
|
| That doesn't make it any less artificial does it? If anything
| that makes it more so. That is also again only speaking to
| supervised learning (and only the fraction of it where
| datasets are curated not collected) not AI in general
| Cybotron5000 wrote:
| I agree - it's kind of lazily framed/worded. A.I. is a vast
| field after all?
| Husafan wrote:
| Discuss amongst yourselves.
| eunos wrote:
| It should not matter whether it is artificial or intelligence or
| neither, as long as it get the jobs done.
| simonswords82 wrote:
| Except when people are using the term AI to sell nonsense that
| doesn't do what it says on the tin.
| stephc_int13 wrote:
| I agree with most of the content of this article.
|
| Especially this part:
|
| "Ethics are necessary, but not sufficient. More helpful are
| questions such as, who benefits and who is harmed by this AI
| system? And does it put power in the hands of the already
| powerful?"
|
| Distribution of power is the most important political question,
| more important than distribution of wealth or what we call
| ethics, that is always biased and hard to measure.
| dec0dedab0de wrote:
| I agree, but wouldn't those questions, or atleast any opinions
| on the answers to those questions, just be part of ethics?
| spoonjim wrote:
| But anything that benefits Microsoft, one of the most powerful
| companies in the world, is necessarily putting more power in
| the hands of the already powerful.
| ThalesX wrote:
| > But from the beginning there was pushback and more recent work
| shows there is no reliable correlation between expressions on the
| face and what we are actually feeling. And yet we have tech
| companies saying emotions can be extracted simply by looking at
| video of people's faces. We're even seeing it built into car
| software systems.
|
| I've been involved with a startup where the CEO was certain we're
| only a step away from replacing humans in recruiting healthcare
| workers with AI analyzing expressions in self-submitted interview
| videos, as well as analysis of quizzes. I got pushed aside from
| that startup for being the 'obnixous technical humanist' (which
| is a caracterization I wear proudly).
|
| > AI is neither artificial nor intelligent. It is made from
| natural resources and it is people who are performing the tasks
| to make the systems appear autonomous.
|
| I've argued as a technological consultant times and times again
| that the effort they are spending on automating humans out, would
| be better spent in making those humans happier and augumenting
| them with an automated system. The fact that the CEO saw his low
| level employees as just temporary assets ready to be replaced
| meant that their life in the company was miserable.
|
| > This April, the EU produced the first draft omnibus regulations
| for AI
|
| I digress. I'm wondering what they're plan of action is since it
| was a European startup and since April we're theoretically pushed
| away from using AI to gauge candidates and all of their VC
| investment money came with the promise of 'automating healthcare
| recruitment and scaling globally'.
| mjburgess wrote:
| > Time and again, we see these systems producing errors ... and
| the response has been: "We just need more data." but... you start
| to see forms of discrimination... in how they are built and
| trained to see the world.
|
| Thank goodness this perspective is getting out there.
|
| I have recently been incensed by the opposite view, that the bias
| is within "the data" only:
| https://www.youtube.com/watch?v=6jbin15-TcY .
|
| This is wholly false. Machines which analyse the world (ie.,
| actual physical stuff, eg., people) in terms of statistical co-
| occurances within datasets _cannot_ acquire the relevant
| understanding of the world.
|
| Consider NLP. It is _likely_ that an NLP system analysing volumes
| of work on minority political causes will associate minority
| identifiers (eg., "black") with negative terms ("oppressed",
| "hostile", "against", "antagonistic"), etc. _And thereby_
| introduce an association which is _not present_ within the text.
|
| This is because conceptual association _is not_ statistical
| association. In such texts the conceptual association is
| "standing for", "opposing", "suffering from", "in need of". _Not_
| "likely to occur with".
|
| There are entire fields sold on a false equivocation between
| conceptual and statistical association. This equivocation
| generates novel unethical systems.
|
| AI systems are not mere symptoms of their data. They are unable,
| by design, to understand the data; and _repeat_ it as-if it were
| a mere symptom of wordly co-occurance.
| TimPC wrote:
| I think it's impossible for a human to read a mainstream body
| of minority political work and not come out with an association
| between black and oppressed. The entire dominant narrative is
| that all minority groups are oppressed. That association is
| definitely present in the text. Maybe it's the case that we
| need to explicitly remove all negative associations for things
| like skin colour (potentially a hard problem in its own right)
| to generate more egalitarian text. But it's not merely a matter
| of AI getting things wrong some negative associations are
| actually present in the text.
| throwaway3699 wrote:
| I don't know, inference from data is literally how all
| decisions are fundamentally made. Why wouldn't it be possible
| to create models that learn this particular pattern?
| bryanrasmussen wrote:
| I think you can create models that learn this particular
| pattern, but the models being told that oppression is a bad
| thing will determine that being black is a bad thing and from
| there that black people are bad.
| mjburgess wrote:
| Here's where the words "data", "pattern", etc. become
| unhelpful.
|
| Learning, as in what we do, is not learning associations in
| datasets.
|
| It is learning "associations" between: our body state and the
| world _as_ we act. It 's a sort of: (action, world, body,
| sensation, prior conceptualisation, ...) association. (Even
| then, our bodies grow and this is really not a formal
| process.)
|
| This is, at least, what is necessary to understand what words
| mean. Words are just tools that we use to coordinate with
| each other in a shared (physical) world. You really have to
| be here, with us, to understand them. Words mean _what we do
| with them_.
|
| Meaning has a "useful side-effect". It turns out when we are
| using words their sequencing reveals, on average, some
| commonalities in their use. Eg., when asking "Can you pass me
| the salt?" I may go on to ask, "and now the pepper". And thus
| there is a statistical association between the terms "salt"
| and "pepper".
|
| But a machine processing only those associations is
| completely unaware there is anything "salt" _to pass_ , or
| even that there are objects in the world, or people, or
| anything. Really, the machine has no connection between its
| interior and the world, the very connection we have _when we
| use words_.
|
| When a machine generates the text "pass me the salt" it
| doesnt mean it. It cannot. There is no salt it's talking
| about. It doesnt even know what salt is.
|
| A machine used to make decisions concerning people, unaware
| of what a person even is, produces _new_ unethical forms of
| action. Not merely just "being racist because the data is".
| pjc50 wrote:
| > A machine used to make decisions concerning people,
| unaware of what a person even is, produces new unethical
| forms of action. Not merely just "being racist because the
| data is".
|
| Powerful and concise, thank you.
| seanp2k2 wrote:
| We also tend to have decades of experience interacting with
| other humans and understanding what would be reasonable to
| want / moral / what would "make sense".
|
| This is a big part of why I'm bearish on things like fully
| autonomous self-driving cars until general AI is achieved.
| Driving is fundamentally a social activity that you
| participate in with other humans, at least until we built
| out nation-wide autonomous-only lanes that only allow
| (through a gate or barrier) autonomous vehicles with self-
| driving engaged in a way that normal vehicles can't "sneak
| in"...I'm not holding my breath. Maybe I'll see it in my
| lifetime (30s), maybe not.
| mjburgess wrote:
| "Bearish" understates my mood.
|
| I think we're drifting into charlatanism, fraud and
| pseudoscience.
| michaelpb wrote:
| Perhaps religious cult territory too. "The Algorithm" [1]
| is already being used to tell fortunes -- which if it
| just stays as a few people having fun with horoscopes I
| don't really care, it seems harmless. But I could also
| totally picture some cryptocoin charlatan getting
| revelations about The Spiritual Algorithm or something
| and starts selling AI for getting into heaven.
|
| ---
|
| [1] By "The Algorithm" I just mean the public
| colloquially reference to trending formulas used on
| TikTok etc, which I'm sure probably uses machine learning
| somewhere
| js2 wrote:
| Relevant presentation by James Mickens:
|
| https://www.usenix.org/conference/usenixsecurity18/presentat...
| mjburgess wrote:
| The speaker starts, "we are computer scientists, we should be
| more sceptical".
|
| This is a little odd: computer scientists are mathematicians,
| not scientists: therein lies the issue.
|
| I think the more this is really absorbed, the clearer things
| become.
|
| * Why are ai models of intelligence _logical_ and not
| dynamical?
|
| * Why are models _rules of association_ , rather than
| interpretative frameworks?
|
| * Why is a statistical association deployed without empirical
| verification?
|
| * Why arent empirical (& verifiable) criteria given against
| predictive systems?
|
| * Against the mechanisms of these systems?
|
| * Why is ML an experimental discipline without experimental
| tests?
|
| * Why doesn't ML research employ tests of significance?
|
| * Why is _the ability to experiment_ seen as incidental to
| intelligence?
|
| * Why is ML sold with a scientific technical vocabulary whose
| meaning is given metaphorically?
|
| * Why arent ML researchers aware these are metaphors?
|
| * Why aren't scientists of intelligence researcher reviewing
| and informing the claims of the artificial intelligence
| community?
|
| * Why do some of the most prominent " _scientists_ " in the
| world advocate that understanding the world is a matter of
| mining datasets?
|
| * Why don't these _scientists_ defend the need for _actual
| science_ in producing accurate models of reality?
|
| * Why aren't computer scientists more sceptical?
| ampdepolymerase wrote:
| I am not sure if you are being deliberately obtuse or
| simply unfamiliar with how ML is designed and implemented.
| Almost every single point you mentioned does happen in
| practice. Most are limited by budget and scale, just like
| real world experiments.
| slver wrote:
| Even in situations where there's an objective reason for AI
| performing poorly with certain groups (like the worse light
| contrast on facial details with many photos of dark-skinned
| people), we go back to default and seek to cast moral judgment on
| someone for being racist, sexist, or something of the sort.
| Because you can't blame AI, we blame the people programming it,
| collecting data for it and training it.
|
| It seems we're trying to prevent a full-on moral panic that would
| be caused by the realization that "discrimination" is an
| objective need by the objective nature of the problem in some
| cases.
|
| For example there are about 14% black people in the US. If you
| have a FAIR SET OF DATA to train from, 14% of those faces would
| be black. No, they're not underrepresented, they're literally
| accurately represented. But this means the AI will be worse with
| those people. So what do we do? If we artificially up the "quota"
| and train with 50% black faces, the AI will now underperform with
| non-black faces.
|
| So then you need to train two networks: black faces and non-black
| faces, and then you need to "discriminate" between both, and pick
| the right network depending on the race you're working with.
|
| Math is racist sometimes.
| ltbarcly3 wrote:
| Ahh so it is naturally occurring without human artifice non-
| intelligence. Got it. NOWHANI
| t_von_doom wrote:
| > The idea that you can see from somebody's face what they are
| feeling is deeply flawed. I don't think that's possible.
|
| I agree with most of the article but this point I disagree with.
| Non verbal communication is a huge component in how we interact
| with each other.
|
| Covid example: When on calls where no cameras are on I get far
| less feedback when presenting to a 'room' compared to if I was to
| present in person or have cams on, even if the room stay silent
| in both situations.
|
| By looking at faces I can see who is distracted, who looks
| confused and whether what I am saying is being received well or
| poorly. Did that joke get smiles (polite or genuine ones?) or eye
| rolls?
|
| Now - do I think the current SOTA algorithms are at this level of
| nuance? No, definitely not. But to say it isn't at all possible
| is ridiculous in my opinion
| cocoafleck wrote:
| I believe that people (usually) choose to communicate with
| faces, but just as people can lie with words they can lie with
| faces.
| arcanus wrote:
| "[Microsoft] Unusually, over its 30-year history, it has hired
| social scientists to look critically at how technologies are
| being built. Being on the inside, we are often able to see
| downsides early before systems are widely deployed. My book did
| not go through any pre-publication review - Microsoft Research
| does not require that - and my lab leaders support asking hard
| questions, even if the answers involve a critical assessment of
| current technological practices."
|
| Interesting to note she opens with a veiled attack at Google's
| internal review practices. For those not aware:
| https://www.google.com/amp/s/www.theverge.com/platform/amp/2...
| Aunche wrote:
| >Being on the inside, we are often able to see downsides early
| before systems are widely deployed.
|
| Well they failed to catch that Microsoft Tae would be instantly
| turned into a internet Nazi bot.
| stephc_int13 wrote:
| No so veiled, and fully warranted, IMHO.
| sanity31415 wrote:
| It sucks when facts get in the way of a fashionable narrative,
| but the Google AI researcher was fired for demanding the names
| of an internal review panel who rejected her paper. She had a
| reputation for accusing her colleagues of bigotry and other
| toxic behavior.
|
| The r/machinelearning thread provides a more balanced
| perspective:
| https://www.reddit.com/r/MachineLearning/comments/k77sxz/d_t...
| joshuamorton wrote:
| This needn't be relitigated, but you're at least misleading,
| if not factually wrong.
|
| The "internal review panel" wasn't at least at the time,
| known to be a real process. In other words, the sequence of
| events was that the paper was written and approved via normal
| channels. After this, the authors were informed that they
| needed to withdraw it. Confused, they asked for more
| information (by what process was this decided, and by who,
| was it peers, legal, an executive who wanted to bury the
| paper, etc.) and were stonewalled. To my knowledge these
| questions still haven't really been answered.
|
| I think its very reasonable request to want to know who or
| what caused your paper to get blocked. In normal peer review,
| even when it is blind, you can work with the reviewers to
| update the paper and see their feedback. These opportunities
| weren't readily provided.
| sanity31415 wrote:
| So it's normal to demand the identities of a blind review
| panel?
|
| What exactly did I say that was "factually wrong" as you
| claim?
| joshuamorton wrote:
| > So it's normal to demand the identities of a blind
| review panel?
|
| It seems reasonable to ask "wait what blind review panel"
| when one appears out of thin air.
| sanity31415 wrote:
| She did a lot more than that. Here is the email that got
| her fired:
|
| Hi friends,
|
| I had stopped writing here as you may know, after all the
| micro and macro aggressions and harassments I received
| after posting my stories here (and then of course it
| started being moderated).
|
| Recently however, I was contributing to a document that
| Katherine and Daphne were writing where they were
| dismayed by the fact that after all this talk, this org
| seems to have hired 14% or so women this year. Samy has
| hired 39% from what I understand but he has zero
| incentive to do this.
|
| What I want to say is stop writing your documents because
| it doesn't make a difference. The DEI OKRs that we don't
| know where they come from (and are never met anyways),
| the random discussions, the "we need more mentorship"
| rather than "we need to stop the toxic environments that
| hinder us from progressing" the constant fighting and
| education at your cost, they don't matter. Because there
| is zero accountability. There is no incentive to hire 39%
| women: your life gets worse when you start advocating for
| underrepresented people, you start making the other
| leaders upset when they don't want to give you good
| ratings during calibration. There is no way more
| documents or more conversations will achieve anything. We
| just had a Black research all hands with such an
| emotional show of exasperation. Do you know what happened
| since? Silencing in the most fundamental way possible.
|
| Have you ever heard of someone getting "feedback" on a
| paper through a privileged and confidential document to
| HR? Does that sound like a standard procedure to you or
| does it just happen to people like me who are constantly
| dehumanized?
|
| Imagine this: You've sent a paper for feedback to 30+
| researchers, you're awaiting feedback from PR & Policy
| who you gave a heads up before you even wrote the work
| saying "we're thinking of doing this", working on a
| revision plan figuring out how to address different
| feedback from people, haven't heard from PR & Policy
| besides them asking you for updates (in 2 months). A week
| before you go out on vacation, you see a meeting pop up
| at 4:30pm PST on your calendar (this popped up at around
| 2pm). No one would tell you what the meeting was about in
| advance. Then in that meeting your manager's manager
| tells you "it has been decided" that you need to retract
| this paper by next week, Nov. 27, the week when almost
| everyone would be out (and a date which has nothing to do
| with the conference process). You are not worth having
| any conversations about this, since you are not someone
| whose humanity (let alone expertise recognized by
| journalists, governments, scientists, civic organizations
| such as the electronic frontiers foundation etc) is
| acknowledged or valued in this company.
|
| Then, you ask for more information. What specific
| feedback exists? Who is it coming from? Why now? Why not
| before? Can you go back and forth with anyone? Can you
| understand what exactly is problematic and what can be
| changed?
|
| And you are told after a while, that your manager can
| read you a privileged and confidential document and
| you're not supposed to even know who contributed to this
| document, who wrote this feedback, what process was
| followed or anything. You write a detailed document
| discussing whatever pieces of feedback you can find,
| asking for questions and clarifications, and it is
| completely ignored. And you're met with, once again, an
| order to retract the paper with no engagement whatsoever.
|
| Then you try to engage in a conversation about how this
| is not acceptable and people start doing the opposite of
| any sort of self reflection--trying to find scapegoats to
| blame.
|
| Silencing marginalized voices like this is the opposite
| of the NAUWU principles which we discussed. And doing
| this in the context of "responsible AI" adds so much salt
| to the wounds. I understand that the only things that
| mean anything at Google are levels, I've seen how my
| expertise has been completely dismissed. But now there's
| an additional layer saying any privileged person can
| decide that they don't want your paper out with zero
| conversation. So you're blocked from adding your voice to
| the research community--your work which you do on top of
| the other marginalization you face here.
|
| I'm always amazed at how people can continue to do thing
| after thing like this and then turn around and ask me for
| some sort of extra DEI work or input. This happened to me
| last year. I was in the middle of a potential lawsuit for
| which Kat Herller and I hired feminist lawyers who
| threatened to sue Google (which is when they backed off--
| before that Google lawyers were prepared to throw us
| under the bus and our leaders were following as
| instructed) and the next day I get some random "impact
| award." Pure gaslighting.
|
| So if you would like to change things, I suggest focusing
| on leadership accountability and thinking through what
| types of pressures can also be applied from the outside.
| For instance, I believe that the Congressional Black
| Caucus is the entity that started forcing tech companies
| to report their diversity numbers. Writing more documents
| and saying things over and over again will tire you out
| but no one will listen.
|
| Timnit
| joshuamorton wrote:
| You have now changed the reason you're claiming she was
| fired, first it was asking about people's identities now
| it's that she wanted people to stop working. This leads
| me to believe that perhaps you reached your conclusion
| first, and are trying to find evidence to support your
| preferred result rather than the other way.
|
| But even now your claims are at best only half true,
| reading the email she says people should stop working _on
| DEI_ things specifically, as they can 't be successful
| without executive sponsorship and execs only pay lip
| service. Is that criticism wrong?
|
| But don't let truth get in the way of a good story,
| right?
| sanity31415 wrote:
| Nope, that falls under:
|
| > She had a reputation for accusing her colleagues of
| bigotry and other toxic behavior.
| joshuamorton wrote:
| She's not accusing an colleagues of bigotry, she's
| stating leadership doesn't support diversity initiatives
| enough and this leads to rank and file employees wasting
| their time. How is that toxic behavior?
|
| And again, what does any of that have to do with a review
| panel? That's what you claimed she was fired for, the
| review panel.
| antonzabirko wrote:
| Good, glad google is facing at least some sort of consequences
| as minor as they might be. It might even end up snowballing
| against them.
| ocdtrekkie wrote:
| My first thought was that even giving this brief interview
| would be a fireable offense for Google's AI Ethics team
| members.
| version_five wrote:
| > ImageNet has now removed many of the obviously problematic
| people categories - certainly an improvement - however, the
| problem persists because these training sets still circulate on
| torrent sites [where files are shared between peers].
|
| This is the scariest part of the article. The idea that some
| central authority should be censoring and revising datasets to
| keep up with political orthodoxy, and we should be rooting out
| unauthorized torrent sharing of unapproved training data.
|
| From a technical point of view, the common reason we pre-traini
| on imagenet is as the starting point for fine tuning for a
| specific use case. The diversity and size of the dataset makes
| good generic feature extractors. If you're using a ML model to
| identify people as kleptomaniac or drug dealer or other
| "problematic" labels, you're working on some kind of phrenology
| and it doesnt take an "AI ethicist" to know you shouldn't do it.
| But that's not the same as pretraining on imagenet, and certainly
| doesn't support trying to make datasets align with today's
| political orthodoxy.
| TOSSAWAY_1 wrote:
| Lol, ai is racist
|
| Therefore the people making ai are racist.
|
| Smart. I'll weaponize accordingly.
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