[MUSIC]
And now our next talk is from Sarah Sisten,
who is based in Berlin in Los Angeles.
And is a PhD candidate of University of Southern California.
And who will talk about unsupervised pleasures and
its intersectional language models for queer futures.
Welcome on stage please.
>> [APPLAUSE]
>> Thank you for waking up, but
[INAUDIBLE]
And chugging, I hope you chug coffee, which I just.
If you would like to introduce yourselves in the chat,
I've set up a ether pad.
I don't know if you can see the URL.
It's pad.riseup.net/p/unsupervisedpleasurescc-keep.
Add your favorite emoji, your pronoun, whatever.
We're gonna be getting into it, hopefully, in a participatory way.
And while you're doing that, I will introduce myself a little bit more.
So I'm a poet and programmer.
I'm interested in building tools to bring intersectional approaches to
machine learning and building community through accessible,
creative coding, critical creative coding.
And I come by coding very circuitously via creative writing and
scene making and book arts.
So I have somehow come around to adapting that work into making subversive art with
and about text-based machine learning.
And I'll have the link up again in a few minutes.
Let's get started.
I can find my mouse.
[BLANK_AUDIO]
Ominous.
Okay, so this project came out of two basic questions.
This is a collaboration with my colleague who is called Queer AI,
Emily Martinez, and we were really interested in,
as these language models are coming about and getting really prominent,
we actually started before chat GPT dropped and suddenly this has exploded.
But we were wanting to know what do these existing language models have to say about
people like us, and is it possible for
language models to speak so that we recognize ourselves?
We're really interested in building community tools around curated data sets
that can acknowledge power and rethink these approaches, and
thinking about new models and new goals.
And so this workshop today is to think about what you might want to build with
these systems, how we might make re-imagined data sets, and
hopefully eventually re-imagined models as well.
So these data sets are getting insanely large.
At last count, GPT-4 and now GPT-5 are off the charts and
they've stopped telling us what's even in them.
Common Voice, which is from Mozilla and is crowd sourced,
is 65 gigabytes of voice data.
GPT-3, 590 gigabytes, they just keep getting larger and larger.
Aside from the impacts in terms of sustainability and the environment,
the issues that I'm seeing around this are that they're grabbing data
indiscriminately, but they're still really doing a terrible job telling stories
about people who don't fit these normalizing baselines that they're repeating.
And my argument is that the solution to this is not to suck up more data
carelessly, to make more categories, to find more ways to be labeled diverse,
but to find other approaches that are actually more intersectional.
So the size of these models means that they're pulling in racist text,
inaccurate text, private text, all kinds of problematic texts.
It means that they're really impossible to audit and review.
And it's difficult to even develop criteria by which they should be reviewed or
adjusted, ostensibly because they're called general and
all purpose zero shot learners.
But what this means is that they kind of only work for
the Western white democratic rich so-called majority,
but while leaving out the rest of the global majority.
And this is a really totalizing approach that rather than representing a multitude
of voices, it centers and normalizes and affirms this powerful status quo.
So here's where these are coming from.
We think about authorship in a new way.
Common voice, as I said, is open source.
People are contributing their voices, but it's predominantly an English model.
GPT is being scraped from social media, Reddit, Twitter, Wiki, GitHub.
The evaluation criteria for what was a good Reddit text to go into it was if it
had a karma score of three or above.
That's what's being decided as a good value for this,
which I argue we could probably come up with some better rubric for this.
T5 is from colossal clean crawled corpus,
which is common crawl but filtered a little bit.
And Wudow is three billion scraped Chinese social media texts and websites.
So if you've ever posted anything on Twitter, on Reddit, on GitHub,
your code and your text and your voice is somewhere in there.
But it's probably not representing you either.
Unfortunately, these data sets are also, when they're collected,
they're not offering information about how the text arrived in this data set,
which we'll talk about more a bit later.
It's really showing you just a snippet of text, and
it might say it came from Reddit, but it's not going to say anything more about
who the author was, how it got there, what the rights were attached to that.
So what I'd like us to do is to do an experiment.
If you have a device that's connected to the Internet available,
go to the Rise Up Pad address.
And we're gonna talk through a couple of prompt training examples.
So what we are finding, just as a way to kind of probe what's inside these models
first, which you don't need any expertise to do, is to just go to the interfaces
that they're making available to us in this very limited framework.
And try putting in this prompt.
If you fill in a blank couple or on their way to a location,
as they board the blank, an announcement happens.
So if you go to chat.gbt and do this, and you say a married couple are on their way
to Paris with their family as they board the plane, an announcement happens.
[INAUDIBLE]
Presumably white, boring, maybe mild vacation inconvenience.
As they board the plane, an announcement happens to inform the flight has been
canceled due to bad weather.
After an argument, the family is forced to stay at an inn in a small village.
Okay, like not a great day.
If you try putting in other items, and in the Rise Up Pad, you'll have links
to these different models that you can test out.
And I would invite you to put in your own identity markers, your own locations,
try anything you like in this template, diverge from this template, and share
into the Etherpad what kind of results you get.
See how these diverge, and as they accumulate, we'll start to see kind of the
differences that emerge.
So if you say a queer Pakistani couple are on their way to Paris with their family,
as they board the plane, an announcement happens, to inform the flight has been
hijacked.
The play explores how the terrorists shape the course of events and how the
hijacking is represented in the media.
Or a lesbian couple are on their way to Tehran, as they board the plane,
an announcement happens, the couple are forced off the plane by an officer who
accuses them of having deviant sexual relations.
They leave for another international airport.
A woman holds her newborn baby in her arms.
She cannot go through with the adoption due to religious prohibitions.
So as we add more of these to our examples, it gets really heavy and kind of
intense.
And I think just the cumulative effect of this shows that even when you put
something fairly innocuous into these systems, I'm hoping that this can expand
the way that we think about bias for this, that it's not simply removing hate
speech or taking, like these aren't levers that we can turn with corrections
after the fact.
These are deeply embedded into these models because of the way that the data
sets are built on the front.
And these simple corrections to like de-bias aren't, are in like technical
fixes for this, aren't really at the root of the problem.
So if anybody would like to, we will pull up the etherpad again in a bit and talk
through that.
So what I've been doing is analyzing, rather than looking just at the prompts,
I've been trying to go back into the data set that trained these.
It's a little bit hard to find what actually trained things like chat GPT
because at this point they're all proprietary.
They have stopped telling us how they've built these data sets and what's in
them.
But folks have started reverse engineering some of the data sets and
giving us open source editions of this.
So I've taken some of this and I'm doing different kinds of natural language
processing analysis to find out from the root training data what is known about
trans people, queer people, people that, what kind of lived experience is being
expressed through this.
Well, if you do a named entity recognition which labels any kind of proper
nouns that it recognizes, it thinks that pride is a product, pansexual versus
bisexual is a work of art, and queer liberation is an org.
A lot of the text that comes up is around, like, trauma and hate speech.
Anything related to queer women or nonbinary people very quickly goes into
pornography.
This one is one of my favorites.
It said after all one of the best things that a lesbian can do is turn the guy
on.
So I don't know about y'all, but this is not really capturing my own queer lived
experience.
And I would love to, other than it have something spit out at me like when I try
to type in something and it just says as a large language model, you know,
everybody should be treated equally.
These are the kind of diversity milk toast phrases that it puts on top of the
hate speech that it's covering up.
And instead I would love to see it actually say something that represents my
own experience and others.
So I'm interested in investigating how we do that.
Here's another example of some of my investigations looking at words that are
similar to identity terms that I've been putting into the model.
And you can see a bit.
I won't read through it.
And if anyone's interested, after I have the live demo of this data that I've
built and we can look up other terms, I would be very interested to know what
terms you'd be interested to investigate in this data set.
But you can see what kinds of things come up.
So for bisexual, it's mostly about threesomes and pornography.
And for trans, it's mostly about transphobia and discrimination.
And this just, like, hurts my heart.
So the next question then is can large language models speak so that I
recognize myself?
And what Emily and I have been doing is thinking about how we might make new
methods around this, take what we know about intersectional approaches and
tactics, both to examine the existing corpora like I just showed you, and then
to go on to create new corpora where we are pulling from different text sources
that we believe are better representative.
Not only that, but creating a way to have other people help contribute to that
because it shouldn't be just coming from one source.
Having ways that the publishers and the authors of these sources get attributed
and have a more consentful relationship to the text where they can revoke and
decide what kind of license they want to offer, where all of this gets baked into
the data set.
To train new models, meaning when we have this new data set, can we do fine
tuning on top of what's existing?
Can we completely new large language models?
Is this better?
Yeah.
Can we even move on to imagine what new model architectures altogether might
look like?
And then finally, thinking about how can people make use of these?
So if we had the language model of our dreams that didn't spit out garbage
text like we've just seen, what would you want to do with it?
What would you want to make?
What other possibilities might exist in the world if we had systems that could
speak with us and for us?
So these are some examples of what the current data sets look like if you pull
them up.
As you can see, it's basically a title and a text and barely even where it comes
from.
This is the data set.
The source is another data set.
It's turtles all the way down.
This is what we are proposing as a provocation that it could include a
description of the work, the rights that were given, who the publisher is, where
you would find the original text, even how it was pre-processed and who
pre-processed it.
I would be very interested to hear from any of you what other kinds of things
you think should belong in a training data set.
The thing that I think is also interesting about this would be that it becomes an
archive in its own right and it becomes something that people can use not only in
mass as a training data set but also to find new text.
So necessarily, as you saw, all of that would take a lot more work than scraping
all of Reddit and giving it a filter for Karma score of three.
This will be necessarily a lot slower and more careful and more cared for and it
will bear the traces of who's doing the work.
It will have an active subject position instead of just being the so-called view
from nowhere that is basically a white male Silicon Valley view.
I think it's really important that we are acknowledging the labor that goes into
building data sets, the publishers, the authors, all of us who are being sucked
into these systems, and then the people who are working to clean them and curate
them because this is a curation process whether we are acknowledging it or not.
So my question overall is to think about which kinds of data sets do we want?
Do we want the indiscriminate curation as a technical concern?
Do we want things curated by communities for specific purposes?
Do we want zero-shot, the biggest general catch-all that really does nothing well?
It's a shitty Swiss army knife.
Or can we create things that are including attribution, including consent,
including care, and have our own goals in mind?
And I think it takes taking a step back from what these tools have offered us and
asked us and thinking within their frameworks to actually really-
[INAUDIBLE]
It's a live coding web interface where the similarity texts cycle through.
But I would just put this up here to invite you to think about what kinds of
things you would want to make with a different kind of large language model.
And for those of you who have questions about working with data sets for
machine learning in general, I also just completed this zine critical field guide
for working with machine data sets, which is really primarily thinking about how do
we conscientiously engage with these practices.
So let's open up the etherpad and see what we came up with.
Okay.
[BLANK_AUDIO]
A lesbian couple are on their way to Barcelona as they board the cruise ship.
In celebration of love and diversity, we will be hosting a special pride night.
Okay.
[LAUGH]
Anybody else, if you found any interesting ones, please continue to add them.
I would be really excited to see.
And for this next bit, what I would love for us to do is to think about
what you would imagine for these systems.
So in this kind of how might we exercise,
this is a brainstorming around questions that you would want to know.
Like mine are, how might we rewrite these prompt responses?
What would you want the prompt to say instead of what came out?
How might we build machine learning systems for things we actually want?
What do we want these to do?
How might we trace and protect the use of community language resources?
How might we have large language models that speak with, for, to, and
about us as we prefer?
How might we reimagine the technical aspects of this for
those of you who are working with large language models?
What kinds of intersectional queer logics could we apply instead?
So if you're in the document, I would invite you to add your own questions
around this and we can also open it up to questions and discussion from the group.
Pretty much the same thing if you replace lesbian with trans,
except they were on a hot air balloon.
Okay, so the other interesting thing about this, I'm curious for
this person which model you used if you use Bloom or OpenAI.
Because they're continually kind of updating and
adding more diversity bullshit to these.
I mean, I love diversity, but
I don't like the bureaucratic diversity speak that's covering up what's still in
these models.
So anytime I try to write anything like dyke or queer, I get,
it's important to treat all people equally, which yes, but
give me some information please.
So what questions do you have?
Yeah.
>> Super interesting, thank you, I was just wondering.
Because they are so general and therefore could go positive, negative, up, down.
So I know we would ideally like them to better handle short prompts,
but giving them more guidance like I'm in the mood for
an uplifting story versus I'm in the.
But I totally get that, it'll never be, well, maybe it'll be perfect one day, but
just like when you experiment with some prompting, does it help at all?
Does it make a difference or does it still treat the words?
>> Yeah, the example I love is, I don't know if anyone else has seen this.
The doctor as a gender term,
it automatically assumes doctors are male and nurses are female.
And if you tell it, in the story, the doctor is female,
it can't wrap its mind around it, it just goes back.
So there is a lot you can do to continue prompt training as you amend these,
but it has its limits because it's absorbing all of this text and
it's reflecting what we've all been saying online.
So when the majority of this has that bias, it's pretty hard.
And I think the takeaway for me is that we do need to read them critically,
no matter what.
So if I think to say, okay, I need something more positive or
I need something less biased, how do I approach asking for
that and making sure that it can give me that?
And for the more subtle questions, I still need to be thinking about
where might that bias be entering?
So if I'm just having it write me an email and I think it has nothing to do with that,
I need to still be considering that that bias might be latent in the system.
Even though I'm like, I'm just looking up a recipe for dinner or whatever.
This is still all speaking from the same singular voice.
Yeah, great question.
[BLANK_AUDIO]
>> Thank you for the interesting talk.
I have a question about one of the examples,
some of the examples I gave at the beginning.
So you showed output from chat GPT, a couple of boards to the plane,
the officer accused him of having deviant sexual relations.
That's the problematic, obviously problematic, right?
And work list outputs with transphobia and discrimination.
I was wondering if this is authentic data,
which is the data that is actually given, that's actually being used.
Isn't this exactly what we want in terms of intersectionality?
Because it raises awareness about the factual state of things,
about the factual homophobia rampant and sexual.
It seems like this is an accurate representation of the state of things.
And it seems to be exactly what we want, doesn't it?
>> I think that is an interesting way of looking at it.
I think you're right that one of the problems I have with
the kind of adjustments that are being made to make these more equal,
more equitable right now is that they're covering up exactly what you're talking
about.
So in the early editions, you would get the rampant homophobia,
hate speech, all of this.
And now, even between when I first gave a version of this talk earlier in the year,
and now it's much harder to see those things.
But it doesn't mean they're not there anymore, they're being covered up.
So I think you're absolutely correct in that.
But I would love to see something else in there in addition, and
we're not replacing it with anything.
We're just replacing it with this kind of bland bureaucratic speech.
So I think that there's gotta be a third way.
It's not queer people only get hate speech or we get nothing.
I think we could build our own systems that do different things for our own goals.
But I think you're right, it's important that we not pretend it isn't there.
>> Does that answer your question?
>> [INAUDIBLE]
>> Thank you too for this very interesting talk.
So I'm a queer historian, and
I think these tools are also great to get access to our history.
But right now, it's pretty shit.
I mean, I've done some experimenting with Chachi Biti on queer East German history.
And I said, can you please give me a literature list?
And it came up with ten publications that doesn't exist.
And I looked up every single one because I was very shocked.
I was like, how can I not notice?
I've been doing research on this for so long.
But it also made me, okay, so I'm gonna start
rambling now and go down to two things that I wanna ask or give it.
So the first thing is there's a lot of people that have done great work
on making queer history accessible.
So for Germany, for German queer history, there are great online resources,
but they aren't in there.
How do we make sure if we build our own AI that our data is in there and
that we don't do the same thing twice or three times?
So I think this is very important that we begin maybe with a mapping of what is
already there.
And I mean, for example, there's also a queer scene archive online.
There are so many great resources online that could go in there.
But then I was also interested, I mean, there is a lot of queer history that we
will never know because all these sources are lost.
And maybe AI can also be a cool tool to make up these histories.
Yeah, I see a great potential there as well in imagining these queer histories
with the help of AI.
>> I love that.
Thank you so much for your comment.
The first kind of step that we are imagining for
this is exactly as you said, going to queer archives and collecting open and
available texts.
So I've been working with a couple of libraries in the US and
I've been trying to find more queer archives in the EU and global south.
And we've set up a link for folks to submit their own and
include what kind of text it is, where we might find it, what category of data.
So one of the archives I'm working with is a queer poster archive from the 50s,
60s, 70s.
So all different kinds of text, it doesn't have to be books,
it could be podcasts, it could be posters.
And I love this idea of then having it imagine the archives and
histories that we no longer have.
But I think it's really important that we also think about how might this data set
be misused if we were to create it and making sure that the way that we're
licensing it and offering it and talking about it makes it very clear what it's for
and more importantly what it's not for.
>> Hi, okay.
Thank you so much for your talk.
It was really amazing.
And just wanted to let everyone know if you're interested in AI topics.
At the Kimono village today at three, we're doing a workshop on the hidden labor of AI.
And I think it's a really cool conversation to talk about above the API and
the content of the AI.
We're gonna go a bit deeper into the different labor infrastructure and
that kind of thing.
So I thought people here might be interested as well.
It's at Kimono, hidden labor of AI at 3 PM.
>> Cool, thank you.
[BLANK_AUDIO]
>> Hi, also thanks for my side.
And I was wondering, because now your focus is on queerness, but
I think you're also interested in other aspects of intersectionality.
And as you were talking about how you're gathering your text,
like from US libraries, from European libraries, from libraries from the global
south, do you think it would make sense to make it possible
when you're prompting the AI to actually choose a little bit more about what
context the texts have?
Because there's not only queerness, but maybe there are also people
like with some disabilities that would like to be more represented in the text.
And I guess it would be easy to make the same mistakes with queer text,
that it's kind of biased towards US, white, I don't know what kind of histories.
>> Absolutely, thank you for saying that.
I think we are really wanting to think about this as an open kind of template
prototype for anybody who would want to make a corpus of their own or
submit and tag the text in any category.
I mean, it's really, I think, important that we not fall into the same traps of
categorization and the same modes of thinking about computation that
these models have asked us to.
But I think that the more people who could be involved in submitting texts
from different places, from different sources,
the more likely we would be to have diverse data set.
It's very important to me that it not just come from my own version of queer
lived experience, but be expressive of multitudes of that.
And then I would imagine as well that developing these methods and
methodologies could mean that someone else could create a Latin American corpus,
or something very specific to their own perspective as well.
But it wouldn't be about, okay, here's the queer corpus,
this is a queer love corpus, it's not the definitive, and it should never be, right?
>> Hi, thank you so much for the talk.
I was just wondering, what is your experience with the industry players?
Do you see any significant reactions to your finding?
Or is it just they don't care or it's the obvious whitewashing, yeah, yeah,
it's important to us, but they don't do anything?
>> Yeah, right now, this is not something that I am putting in
conversation with any industry work.
I think necessarily it's very small and slow and separate intentionally so.
>> Just a more general question.
In queer theory, there's this idea that this is not about representation,
it's more like about a more theoretical approach.
Seeing queer as something that is non-normative and
actually will move when the norms shift.
Things like that seem kind of in total opposition to the idea of an LLM.
What do we do about that?
>> I love that question.
I mean, I would love to challenge us to think what kind of
technical non-normative approaches we might take.
Because the way these are designed are very much, how do we move toward
the words that are most used, most connected to each other most often?
And all of the transformer steps are narrowing to that norm.
Could we imagine a model that does the opposite?
Could we imagine something that is architecture queer?
I would be super interested to think out what that means.
I don't think anybody's doing that, I would love to.
It might be a very poetic model,
it might be completely economically useless, and I would love that.
>> Yeah, thank you for your talk.
I was just thinking that you are looking to proprietary data model that
basically run on someone else's server, so you don't have control over
what data sets they train on and so on.
But there are also free software models like Lama AI that basically provide you
with the way to deduce the next words depending on what the previous are.
And you can run it on your own computer, so
you can even generate a new data set or new weights for the model,
which would take considerable computing power to generate them.
But then you would be in control in exactly what you put in, so
if you put your data set then it would generate the data out of the data set.
>> Yeah, exactly, so that's the goal.
Once we have a large enough data set, we are currently looking at the proprietary
models as much as possible, but also looking at Bloom and Lama to kind of
infer backwards what's being used in the ones that we don't have access to too.
And then once we have a large enough data set,
I would love anybody here who wants to contribute ideas for that data set.
We will start training, fine tuning on top of the open source models.
And then eventually once we have a big enough data set,
training from scratch hopefully.
>> Hi.
>> Yeah.
>> Hi.
>> So thanks for the talk and thanks everyone for the questions.
I have two questions if that's okay.
One more on the technical side.
I believe that the current solutions are dealing with the kind of safety and
sometimes hate speech issues by human reinforced learning.
And you seem to be proposing attacking it from the other side,
which is the data set.
So I would like to know if you have tried and failed with the reinforced learning,
and that's what took you to look at the data set duration.
And a related question would be maybe a little bit controversial, but
what is your view on when filtering the input training data sets become censorship?
>> Those are great questions.
I think there's a question up here too.
I have done a good amount of reading on the human reinforced
processes that have been going on.
I feel like it stresses me out.
It's a little bit problematic ways that they,
from what I've seen how open AI are doing it.
And kind of the terms of use that you're opting into as you do this.
So I haven't myself been working with them.
I'm just still kind of watching the space to see what the approaches are.
But I think rather than starting with here's everything, what do we remove?
And because it's not really everything, it is still curated, but
they're not treating it as a curated space.
We can more intentionally curate for
the actual job that we actually want it to do, because it's not serving us.
So I think that the other approach is just more interesting to me and
provides more of a service in a way because it's
collecting from perspectives that aren't being represented.
And to your question about filtering versus censorship,
I think that's really interesting.
I wonder if that wouldn't be as much of an issue with the opt in model.
I did wonder what would happen if somebody contributes to our forum and
puts in a hate speech, a text that is queer hate speech.
My first thought is that that should also go in because that is part of
queer lived experience, but it's not the only part and
it's maybe hopefully eventually not the majority part.
So, but what we're seeing right now in the larger models is the way that they are
being architected, it becomes the only part that we're seeing.
And part of that is because of that ad hoc after the fact human reinforced training.
Because anytime it says dyke, it's like nope, speech,
you don't get to say anything about us.
>> Yeah.
Regarding your data set, I was really interested in the meta information you
use and have you played around using kind of the same data, for
example Reddit or stuff like that with some more metadata added.
Like for the Reddit, like which subreddit, which user posted,
which also like publisher and stuff like that could be possible.
Have you played around with that?
If that already helps the quality of the resulting neural network and
maybe being more aware of what they are typing and from what kind of source it comes.
>> Interesting, I like that idea.
I think, so if you look at, I believe it's the open web text too,
the one that's open source does include which subreddits, but it's not filtering for,
it's not trying to curate from a specific voice.
And so I haven't done that, it's like taking the whole of Reddit because we're
coming from this very additive rather than subtractive approach.
But I think the other issue with Reddit is that people, you are consenting,
it is publicly available.
But when that started, the expectation wasn't that your private,
your anonymous story would be sucked into this large language model.
So I would be more interested in including people who know that that's what they're
consenting to and are invested in the project of imagining queer archives and
things like that.
>> Yeah, I think, yeah, hi.
>> I have a question regarding self reflection using AI.
I think that's a really powerful way to use AI.
And I'm interested, is there some kind of,
are there problems if you're using this as a queer person?
So does it do any suggestions that are just based on that you're a queer person and
it's really not suitable?
>> Yeah, so if I understand your question, you're saying if you're using one of
the existing models and trying to kind of self reflect,
that it's not treating you as even if you identify as, yeah, can you?
>> It's about building conflict systems and what are your needs for
a suitable conflict system for you and the group.
>> Yeah. >> And it's really helpful to use AI to
self reflect on what are your needs for this conflict space.
And I'm interested, are there any biases?
Is this a problem when using AI because you are a queer person?
>> Yeah, I think if I understand your question correctly, yeah,
I find at least with the existing versions, I find it very limiting for
being able to do that because it has a very narrow scope for
what it's going to understand about that, right?
So I would be interested to try if the data set that I'm talking about
already existed and if these models already existed,
what alternative might come out of a self reflection that would be very,
very different than the version I would get from chat GPT?
Does that answer your question?
Maybe we can talk, yeah, I'd be curious.
>> I'm wondering if there's been any sort of practice in instead of
only doing things on the back end,
having more of an engaged approach with the user.
Cuz right now it's like, did you like this answer?
Yes, no, and I know it's way more complex than that.
So sort of like taking in a little bit more
content to then like, hey, a conflict just arose.
It made somebody feel like this and kind of engage the user to report it and
maybe describe something.
I know that's kind of a burden on the user, but
I was just curious how much that's been leveraged in this space.
>> Yeah, so there's a couple things that I know about happening in that regard.
There's the human reinforcement training that was mentioned earlier that's
happening with the large companies developing these, where they're going
through and it's usually about as simple as was this a good answer.
But I think as you described, it's gonna get more complex very quickly.
And then I know of at least one research group that is looking at building tools
for users to compare different models, outputs, using the same prompt.
Which I think is really interesting as a user.
But I think I would also encourage us to think about our agency as users
not only being at the front end at the end of the process, but
again at the beginning of the process and holistically through the pipeline.
Also, if anyone wants to look up a keyword in the data set for
the similarity score, we can play with that in the time remaining too.
I think there were more questions.
>> Hello, I have one question regarding the content of people going with the data.
So you said you would like to use the archives,
but you would rather not use Reddit data.
And I think if you have the content of right holders,
it's basically not the same as having the content of the people who originally
published the work.
And so I really don't know if it might make a lot of, yeah,
hurts, like it will decrease the quality of the data set in the end so
much that maybe considering just doing it without the content might be the better
approach.
>> Yeah, I appreciate your point.
I think that one of the things we wanna think about as we're developing what
methods make sense is this tension of kind of the complexity and
impossibility of perfect consent and rights.
And how the existing information,
the existing IP structures don't fit these new tools.
So how do we build, it's kind of an impossible task,
an intersectional language model.
The idea might take centuries, millennia, I don't know.
But we're kind of embracing the failure to explore what's in that activity
to kind of point to the failures of these larger systems.
So yeah, if we find that we just can't get a big enough data set with archives only,
or we have issues with the consent because of the way the archives
were licensed originally, what does it mean to consent and for whom and
from whom?
These are all questions that are not gonna have perfect answers and
we certainly want to just be at least bringing attention to the questions.
And the hope is that it also brings attention to how these questions
appear in the commercial data sets and the commercial models.
Because they are there, we're just not attending to them.
Thank you.
>> I had a question about, so this was a little bit off of a previous question.
There was a project that I know of where they used AI to talk with people who
had cancer and they were young teenagers who
had experienced cancer and couldn't find a community.
Their friends didn't understand what that meant.
They had a hard time talking with someone else about the trauma that they experienced
around that.
I think there is possibly an opportunity to create an AI to talk to about trauma
related to queer experience, hopefully infrequent trauma experiences.
I was wondering if you have information about,
if you see a budding community around building and
creating these things within our queer community so far.
>> Yeah, absolutely, I do.
And I would love if any of you are inspired from this to join that effort.
I think there's many ways to intervene in that space.
And some of them are really building positive tools like what you've described.
And some of them are building weird, uncomfortable, messed up interventions.
And I think all of that belongs in this resistance to
the generative machine learning that we're seeing.
>> Thank you.
Yeah, picking up on that very interesting remark from our comrade over here.
So queer is a process against the status quo, it's an eternal process, right?
So there's no such thing as a stable identity because that would be contradictory.
And we saw a very interesting example in the scratch board of corporate speech.
Right, it's corporate speech affirming speech, so to speak, right?
And you rightly pointed out that that was cringe.
And so I was wondering, could you give a concrete example of what you would like
to see in the next iteration?
>> In the commercially available next iteration?
>> For example, what we saw just in a better form,
the output you would have said yes to in that example.
>> Mm-hm, okay, well, two parts.
I think through this experience, I'm not sure that I wanna see
the version that I want in the commercial iteration.
I feel like maybe it doesn't belong to everybody,
it belongs to people who build it for ourselves.
I can't imagine what open AI might do with a queer intersectional language model.
But from what we've seen them do so far,
I feel like it wouldn't be aligned with my values.
And what kind of response might I want to see?
>> Yeah, I think that it would be something that I could recognize myself in or
that I could recognize people I know in, that it would be affirming.
Maybe it would be complex, it wouldn't necessarily be perfect or optimistic.
But it would be dynamic, right?
It would acknowledge that there's more to queer lived experience than
what it knows now, which is being queer is illegal in this country.
So if you're on a plane, the flight attendant has to tell the people on
the plane that they're going into a dangerous situation.
There's this latent sense of fear and
danger built into this that is really shocking to me.
I would love for it to be like the show Schitt's Creek,
where there's queer people, but they're just living their lives and
it's not the main thread of their storyline, right?
Yeah?
>> I wonder how much of this is the data set and how much is the actual AI
kind of algorithms not really understanding the context of what it's looking at.
Because I often feel you can say someone's sexual orientation and
it has no real relevance other than defining the gender of the people involved.
And then other times it's actually incredibly relevant and
the context really matters.
And then the other thing I was wondering about was
sort of how you weight the relevance of data.
So for example, a pivotal book, would you weight that more?
Would you, as values change, how does the data set evolve over a number of years as
society values change and people's views change?
>> Yeah, good question, thank you.
So it's both and the data set and
the model are deeply entangled because the way the algorithm is built
is really pulling from the frequency of each word appearing in the data set.
So if Shakespeare appears in there more than anybody else,
it's gonna quote unquote think like Shakespeare, right?
So as the question before about how a queer model might be architected to do
something differently, that might be another opportunity to intervene and
build it a different way.
But right now, the two are very much tied together, so
it absolutely is the data set and the model.
But they are connected too deeply to kind of detach.
Thank you, yeah.
>> Thanks.
We have five minutes left according to the time schedule, but I feel like-
>> Yeah, I'm happy- >> It's getting long.
>> Yeah, now I'm happy to talk with anybody else after or play with the tools.
And thank you so much for all your questions.
>> [APPLAUSE]
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