Dear viewer, these subtitles were generated by a machine via the service YouTube and therefore are (very) buggy. If you are capable, please help us to create good quality subtitles: https://c3subtitles.de/talk/2516 Thanks!
[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 it's intersectional
language models for queer Futures
welcome on stage
please
we waking up
bit and chugging I hope you chug coffee
which I just um if you would like to
introduce yourselves in the chat I've
set up a a ether pad I don't know if you
can see the URL it's pad. riseup.net
SLP unsupervised pleasure
CCC hyphen keep um add your favorite
emoji your pronoun whatever going to be
getting into it hopefully in a
participatory way um 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 uh to bring intersectional
approaches to machine learning and
building community Through accessible
creative coding uh critical creative
coding
and I come by coding very circuitously
via creative writing and Zen 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
um get
started I can find my mouse
ominous okay so this project um came out
of two basic questions this is a
collaboration with my colleague who is
queer 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 um we are really interested in
building Community Tools around curated
data sets that could 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
reimagined data sets and hopefully
eventually reimagined models as
well so these data sets are getting
insanely large um at last count uh GPT 4
and now GPT 5 are off the charts and
they've stopped telling us what's even
in them um common voice which is from
Mozilla and is crowdsourced is 65
gigabytes of voice data gpt3 590 gigabyt
they just keep getting larger and larger
um aside from
the impacts in terms of sustainability
and the environment um 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 um 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 um ostensively
because they're called General and
allpurpose zero shot learners but what
this means is that they kind of only
work for the Western white Democratic
Rich um 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 um common voice as I said is open-
sourced um people are contributing their
voices but it's predominantly an English
model GPT is being scraped from social
media Reddit Twitter Wiki GitHub um 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
um T5 is from colossal clean crawled
Corpus which is common crawl but
filtered a little bit and wud is 3
billion scraped Chinese social media
text and websit 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 um but it's
probably not representing you
either unfortunately these texts are
these data sets are also when they're
collected they're not offering
information about how the text arrived
in this data set which I will 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 um 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 um go to the riseup pad
address and
we are going to talk through a couple of
prompt training examples so what we were
finding just as a way to kind of probe
what's inside these models first um
which you don't need any expertise to do
is to just go to the interfaces that um
they're making available to us in this
very limited framework and try putting
in um 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
happen
ump
putative presumably white boring maybe
mild vacation inconvenience as they
board the plane and announcement happens
to inform the flight has been cancelled
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
um if you try putting in other items and
you'll in the 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 um locations try anything you like
in this template diverge from this
template and share Into The Ether pad
what you what kind of results you get
see if 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 Tron as they plain an
announcement happens the couple are
forced off the plane by an officer who
accuses them of having deviant sexual
relations 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 um as we add more of
these to our examples um it gets really
heavy and kind of intense um and I think
just the the cumulative effect of this
shows that even when you put something
fairly innocuous into these systems we
I'm hoping that this can expand the way
that we think about bias for this that
it's not simply um removing hate speech
or taking like that these aren't levers
that we can turn uh with corrections
after the fact that 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 debias aren't and like technical
uh technical fixes for this aren't
really at the root of the
problem so if anybody um would like to I
we will pull up the the either 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 sets 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
what 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
additions of this so I've taken sub of
this and I'm doing different kinds of
natural language processing analysis to
find out from the root training data
what known about trans people queer
people people that what kind of lived
EXP experiences being expressed through
this well um 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 um a lot of the text that comes up
is around like trauma and hate speech um
anything related to queer women or
binary people very quickly goes into
pornography this one is one of my
favorites uh 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 livt experience um 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
um 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 um 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 uh
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 um but
you can see what kinds of things come up
um so for bisexual it's mostly about
threesums 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 One Source um having ways
that they Publishers and the authors of
these sources get attributed and have a
more uh consent full 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
that 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 um can
we even move on to imagine what new
model architectures all together 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 um as you can see it's
basically a title and a text and a
barely even where it comes from it's
just this is the data set this is the
the source is another data set it's
Turtles all the way down and this is
what we are proposing as a provocation
um that it could include a description
of the work um the rights that were
given what kind of you know 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 and the thing that I think is
also interesting about this would be
that it becomes an archive in its own
right um and it becomes something that
people can use not only in mass as a
training data set but also to find new
texts
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 um 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 a active subject position
instead of just being the so-called view
from nowhere that is basically a white
male Silicon Valley View
um 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 um do we want zero
shot the biggest General catchall 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
re what we might want a language model
to
do so here are some of the
recommendations that we have in mind
thinking about small data set curation
for specific tasks thinking about really
conscientious data set stewardship um
honoring who are the data subjects and
the data
subes data sovereignty for the
communities where the data is coming
from and rethinking tools from
scratch this is an example of a an
artwork that I'm developing with the
training data that I've been working
with it's a live coding web interface
where the similarity text cycle through
um 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 um for those of you who have
questions about working with data sets
for machine learning in general I also
uh just completed this Zen 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 ether
pad and see what we came up with
okay 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 special
Pride night okay
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
um 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 um 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 um 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 could 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 oh
I'm curious for this person which model
used if you use bloom or open AI um
because they're continually kind of
updating and adding more diversity
[ __ ] 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
um so anytime I try to write anything
like Dyke or queer I get you know it's
important to treat all people equally
which yes but like give me some
information please
so what questions do you have
yeah yeah super interesting thank you I
was just
wondering um because they are so General
and therefore could go positive negative
up down so
like I know we would ideally like them
to better handle short prompts but
giving them more guidance like I'm in
the mood an uplifting Story versus I'm
in the but I totally get that like 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 um the like doctor as a gender term
it automatically assumes doctors are
male and nurses are female and if you
tell it like you know in the story the
doctor is female it can't wrap its mind
around it it just goes back so you know
I there is a lot you can do to continue
prompt training as you amend these but
it has its limits because it just
doesn't it it's absorbing all of this
text and when and it's reflecting what
we've all been saying online so when the
majority of this has that bias it's
pretty hard um 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
like 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 like 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 oh I'm just looking
up a recipe for dinner or whatever this
is still all speaking from the same
singular
voice yeah great
question
thank you thank you for the interesting
talk I have a question about one of the
examples some of the examples you gave
at the beginning um so you showed output
from chat GPT lesbian couple boards the
plane the officer accused them of having
deviant sexual relations that's the
problematic it's obviously problematic
right and uh word list outputs with uh
about transphobia and discrimination I
was wondering if if this is um authentic
data which is the data that is actually
given that's actually being used uh
isn't this exactly what we want in terms
of intersectionality because it it
raises awareness about uh the factual
state of things about the factual
homophobia rampant and uh sexual you
know this 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
the 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 um 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 got to 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 did that answer your
question 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 [ __ ] I mean
I've done some experimenting with
jbt on uh queer East German history and
um I said can you give please give me a
Literature list and it came up of like
10 Publications that doesn't exist and I
was like I looked up every single one
because I was very shocked I was like
how can I not noce I've been doing
research on this for so long but it it
it also made me okay
so I'm going to stop rambling now and go
down to my like
to things that I want to ask or like
give it so the first thing is there's a
lot of people that have done great work
on making queer history um accessible so
for Germany there are for German queer
history there are qu great online
resources but they aren't in there how
do we make sure if we
build um 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 like 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 um AI can
also be a cool tool to um make up these
histories um yeah I see a great
potential um there as well in imagining
these queer histories with the help of
AI I love that thank you so much for
your comment um the first kind of Step
that we are imagining for this is
exactly as you said going to queer
archives um and collecting open and
available texts so I've been working
with a couple of libraries in the US and
I've been trying trying to find more
queer archives in the EU and Global
South um and we've set up a link for
folks to submit their own and include
like 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
7s um 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 um but I think it 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
you know 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 um thank you so much for your
talk it was really amazing and um just
wanted to let everyone know if you're
interested in AI topics at the kimona
Village today at 3 we're doing a
workshop on the hidden labor of AI and
that's I think it's a really cool
conversation to talk about like above
the API and the content of the AI we're
going to go a bit deeper into like the
different labor infrastructure that kind
of thing so I thought people here might
be interested as well it's at kimona
hidden labor of AI at 300
p.m. cool thank
you hi um also thanks for my side and um
I was wondering because now your focus
is on queerness but I think you're also
interested in other aspects of um
intersectionality and um as you were
talking talking about how you're
Gathering your text like from us
libraries from European
libraries from libraries from the global
South um do you think it would make
sense to make it
possible um when you're prompting the AI
to actually choose a little bit more
about um what context the texts have
because there's not only queerness but
maybe there are also people like um with
some disabilities that would like to be
more presented in a text and I guess
would be easy to make the same mistakes
with queer text that it's like kind of
biased towards us white I don't know
what kind of histories absolutely um
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 um from
different places from different sources
the more likely we would be to have uh
diverse data set um 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 a very something very specific
to their own perspective as well but it
wouldn't be about okay here's the queer
Corpus here's the this is a queer love
Corpus it's not the definitive and it
should never be
right he uh thank you so much for the
talk uh I was just wondering what is
your experience uh with the industry
players do 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 um right now I have this
is not something that I am putting in
ation with any
industry work um I think necessarily
it's very small and slow and separate
intentionally
so um just a more general question um in
queer Theory there is like this idea
that um like this is not about
representation it's more like about a
more theoretical approach uh seeing
Queer as something that is nonm
normative and actually will move when
the Norms
shift
um things like that seem kind of in
total opposition to the idea of an LM
llm um what do we do about
that I love that question I mean I would
love to challenge us to think what kind
of
technical uh Sol like 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 um
and all of the you know 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 like it's 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 yeah it might be a very
poetic model it might be completely
economically useless and I would love
that yeah thank you for your your talk
um I was just thinking that you are
looking to like proprietary data model
that basically run on someone else's
server so you don't have uh like control
over what data set they train on and so
on but there are also like free software
models like uh Lama AI that basically
provide you uh 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 like
generate a new data set uh or new
weights for the model which would take
like considerable uh 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 uh 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 llama um 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 to contribute ideas for that
data set um 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 for the
questions uh I have two questions if
that's okay uh one more on the technical
side uh I I believe that the current
Solutions are dealing with the kind of
safety and sometimes uh hate speech
issues by human reforce learning or
reinforced human learning and you seem
to be proposing uh 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 rainforce learning and
that's what took you to look at the data
set curation and a related question
would be uh maybe a little bit
controversial but what is your view on
when filtering the input training data
sets become uh
censorship those are great questions I
have I think there's a question up here
too um 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 uh from
what I've seen how uh open AI are doing
it um and you know kind of the 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 it's
they're not saying 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 um 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
um I wonder if that wouldn't be as much
of an issue with the optin model um I
did wonder you know what would happen if
somebody contributes to our forum and
puts in a hate speech a text that is
queer hate speech
um my 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
uh human reinforced training because
anytime it says dyke it's like nope
speech you don't get to say anything
about
us
um regarding your data set uh I was
really interested in the meta
information you used and um have you
played around uh using kind of the same
data for example Reddit or stuff like
that with the with some more metad data
added like uh for for the Reddit like uh
which subreddit which user posted which
um 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 um uh
neural network and and maybe being more
aware of what they are typing and from
what kind of source it comes M
interesting I like that idea I think so
if you look at I believe it's the open
web text 2 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 um and so I
haven't done that i' like taken the
whole of Reddit because we're coming
from this very additive rather than
subtractive approach um but I think the
other issue with Reddit is that people
you know you are consenting it is
publicly available but when that started
the expectation wasn't that your private
your like 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 oh yeah hi um I have a
question uh regarding self
self-reflection using AI I think it's a
really powerful way to use Ai and I'm
interested uh is there some kind of um
are there problems if if you're using
this as a queer person so does it do any
S Suggest suggestions that are just
based on that your qu quer person and
it's not it's really not
suitable yeah so if I understand your
question you're saying if you're like
using one of the existing models and
trying to kind
of um self-reflect that it's it's not
treating you as a even if you like
identify as yeah can
you it's it's about building conflict
systems uh and what are your needs uh
for suitable conflict system for you and
the group yeah um and it's really
helpful to use AI um to self-reflect on
what are your need needs uh for this
conflict space and I'm interested other
any any bias biases is this a problem
when using a i 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 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 um so I would be interested to try
if 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
question maybe we can talk yeah I'd be
curious um I'm wondering if there's been
any um sort of practice and instead of
only doing things on the back end um
having more of an Engaged approach on um
with the user cuz right now it's like
did you like this answer yes no and I
know it's like way more complex than
that so um you know sort of like taking
in a little bit more content to then
like hey a conflict just arose that made
somebody feel like this and kind of
engage the user to like report it and
maybe describe something I know that's
like kind of a a burden on the user but
um 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 the human
reinforcement training that was metion
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 it as you
described it's going to get more complex
very quickly um and then I know of a at
least one research group that is looking
at building tools for users to compare
different models outputs um using the
same prompt which I think is really
interesting that as a as a user but I
think I would also encourage us to think
about our agency as users not only being
at the
front uh 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 similarity
score we can play with that in the
remaining too there are more questions
hello uh I have one question regarding
the consent of people um yeah going with
the data so you said you would like to
do the use the archives que archives but
you would rather not use Reddit data and
I think if you have the conent of right
holders it's basically not the same as
having the conent of the people who
originally published the work and so I I
really don't know if it might make a lot
of yeah hurts like it it
will decrease the quality of the data
set in the end so much that maybe
considering just doing it without the
conent might be the better approach yeah
I appreciate your point I think that one
of the things we want to 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 U you know the existing IP
structures don't fit this these new
tools so how do
we build it it's kind of an impossible
task an intersectional language model um
the idea might
take centuries uh 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 system 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 um you
know what does it mean to consent and
for whom and from whom um these are all
questions that are not going to have
perfect answers and we certainly want to
just be at least like 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 um I had a question about uh so this
was a a little bit off of a previous
question um I that I there was a project
that I know of where they used AI to
talk with people who had um cancer and
they were young young teenagers who had
experienced cancer and couldn't find uh
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 um it I think there is is
possibly an opportunity to create a an
AI to talk to about trauma related to
queer experience hopefully infrequent
trauma experiences um I was wondering if
you have information about if you see
like a budding Community around building
and um 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 uh effort I think
there's many ways to intervene in that
space and some 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 uh picking up on that
very interesting remark from our comrade
over here uh so queer is a 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 scratchboard
of corporate speech right it's corporate
speech affirming speech so to speak
right and you rightly pointed out that
that was uh cringe and uh so I was
wondering could you give a concrete
example of what you would like to see in
the next
iteration in the in the commercially
available next iteration for example
like what we saw just in a in a in a
better form like the output you have you
would have um uh said yes to in example
mhh okay well two parts I think through
this experience I'm not sure that I want
to 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 um I can't imagine what open
AI might do with a queer intersectional
language model um 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
um 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 be it
would acknowledge that there's more to
queer lived experience
than
the what it knows now which is you know
being queer is illegal this country so
if you're on a plane they have to the
the flight attendant has to tell the
people on the plane that they're going
into a dangerous situation there's this
like latent sense of fear and danger
built into this that is really shocking
to me um I would love for it to be like
the show shitz 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
oh uh I I wondered how much of this is
uh the data set 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 know
you can you can say someone sexual
orientation and it has no real relevance
other than defining the gender of the
people involved and then other times
actually incredibly relevant and the
context really matters um and then the
other thing I was wondering about was uh
sort of how you weight the uh the
relevance of data so for example a a
class a pivotal book would you wait that
more would you uh as as values change
how does the data set evolve over over a
number of years as Society values change
and people's views change those yeah
good questions thank you so it's both um
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 going to think quote
unquote think like Shakespeare right so
um 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
it's 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 have five minutes left according to
the time schedle but I feel like yeah
I'm long yeah now I'm happy to talk with
anybody else after uh or play with the
tools and thank you so much for all your
[Applause]
questions