Of data scientists and AI  from critique to reflective practice (Mario Sosa Hidalgo)

uh
and if you have any questions
during the presentation please use the
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and in the second half hour we will open
the floor for questions and discussion
um and we'll i'll leave it up to mario
if he wants to
make some pauses in the first half an
hour so mario the floor is yours
okay well thanks i'm jenny uh good
afternoon everyone
thanks for for inviting me here my name
is mario sosa sabine mentioned i'm a phd
candidate
at the keen center for digital
innovation the school business and
economics of the variety university
amsterdam sorry for repeating that but i
was part of my presentation
uh then i just let me click yes so
uh the agenda for today of course is
going to present oh my perhaps that's
not necessary anymore
thanks f jenny uh i'm gonna present a
little bit more of the group the
research group that i'm in at the
prairie university university and um
also present you a little bit of my
research which is related
as you might notice with artificial
intelligence and data scientists
in the end we also can have a q a
session
you can please let me know all your
feedback
and also your doubts and questions uh as
i mentioned
as i mentioned to you before my name is
mario sosa
here i am the i am a phd candidate
the king center for the innovation for
the third time funny thing i graduated
from the industrialist engineering
faculty of the tu delft last year
yes still last year yeah last year my
graduation thesis
is called design of financial toolkit
for the development
development of artificial intelligence
applications
i graduated from the master of strategic
product design
uh important thing to mention i am doing
my phd
in the i a i know project which is a
project that was funded by a
now open competition national grant that
was earned by kim
king group the idea of this project is
to answer the question what is the
impact of artificial intelligence in
knowledge work
which i'm going to cover a little bit
later in the presentation
but first let me introduce kim well
she's not kida
she's my promoter uh the king center for
digital innovation is
led by my promoter marlene hussman uh
we are a research group that studies
digital innovation we take
three different areas uh for example the
organization and digital innovation
future work and also it
has to do with management of digital
transformation uh
within that group i belong to a sub
research group
i know it's kind of funny what is called
ai at work which are
these phds uh and postdocs and everyone
related to
artificial intelligence in work setting
what we do is to try to understand how
these practices are changing
uh with the introduction of sorry these
work practices
so our practices are changing with the
introduction of data driven technologies
which i
called ai from now on i'm sorry i know
it's a technical term but i will use ai
as
an umbrella term sorry no machine
learning deep learning specific just
umbrella term uh our methods involve
doing ethnographic studies quite long
ethnographic studies to gain insights
into are the change
changes that organizations have with
these technologies
over time uh we're currently
investigating different cases
uh where these technologies are
introduced into police off and sorry
different work and organization for
example police
in the netherlands also several
hospitals in netherlands as well
human resources of multinational
companies uh
in my case also like uh gonna do
a demographic research in an
organization that
as uh does they do matching job
well there's a consultancy for matching
job or job matching
i think goal scope something like that
so
and that's why i'm going to talk about
my research which is still research in
progress
uh i'm going to try to answer the
question what is the impact of
artificial intelligence in knowledge
work
in my case specifically i'm gonna focus
on data scientists
but before going into that i'm gonna
present you
my personal roadmap a phd because i
think that this is a good opportunity
for
to get feedback from it uh okay i'm
finishing my first year
so one of my deliverables that i got
from this first year a phd is a
conference paper
which has been accepted uh i'm going to
talk about that later
and during the second year i'm expecting
to start with my ethnographic study
organization which i'm going to call
for this presentation matching co which
is uh so daniel
of course it's confidentially we have to
respect that
uh the third year of course nobody knows
what is what happens in the third year
just entering the unknown
uh perhaps some of you might relate and
the fourth year of course
you start entering to panic because you
need to finish your phd
uh for the sake of this presentation i
will focus on on the paper
i think that is a good opportunity as i
told you to present you my research what
i have been working on and i would like
to have your feedback perhaps you can
give me some suggestions
uh you can help me think along this
topic and
yeah i'm going to present you my paper
which is titled data science as digital
work from critique
to reflective practice it's a short
paper research in progress paper
uh this paper has already been accepted
at ifib
joint working conference 2020 which is
called the future of digital work the
challenge of inequality
to be held in in a month in december 10
and 11
of course gonna be online due to the
current situation
uh yes so some of
the yeah i'm gonna begin the
research context that i have the context
of my research starts
regarding the these new professions that
are emerging
in organizations due to ai it might be a
little bit like obvious
that they are now their person data
scientists their engineer machine
learning engineers but
they weren't there before and that this
is something that
has only happened with artificial
intelligence not even with for example
big data or
technologies before that uh there is a
lot of
there's a big fuss around what is the
influence of artificial intelligence
so but most of the debate around that is
either too high level so
ai will automate everything will
automate all of our jobs
or ai will control us or these companies
will control us
as this is too black and white for
example in the
discourse of the surveillance capitalism
concept and what we notice what i notice
is there's
still not a clear understanding of the
practices of these data professionals so
we're assuming too much
we we just get into a high level of what
are the technology can do but
we're now focusing on the people that
are actually well we're not focusing on
the people that are actually doing the
technology
we are also focusing on the people that
are getting affected but
not on the data scientists for example
they might be
unaffected as well and you might be
wondering why this is important
um well first these these professionals
are in charge of building these systems
right with all that this implies
the values the ethics the epistemologies
all these things that
coming from these professionals to the
systems
and they might be reflected on them as
you can see in this car
sorry in this cartoon and also they are
gaining influence in organizations
thanks to this offer of objective
rationality
which is quite attractive for companies
and managers
uh of course this kind of race to power
has been criticized
mostly uh most recently in popular media
with the social dilemma
uh but also in research where there's a
suggestion of
the emergence of knowledge inequalities
which we define these as disadvantages
position that
data science itself this this field has
in organization
because of this rationality speech
uh there are other data science
critiques that come from the literature
for the critical data literate
scholarship or literature
that they also cannot connotate these
advantages
and also some of their consequences i
can present to you
now a table what i did a compilation of
all these critiques
uh there are some certain topics uh
certain important names for examples uh
reeves
uh and susanna socialist
who's like the author of the book the
age of surveillance capitalism
so the critiques go around the
domain agnosticity of data science how
is just go there
absorbing knowledge without actually
giving something back which is related
to this prospecting practices
also related to more social high level
perspective this data extractivism uh
concept and data colonialism concept in
which these authors
argue that you know these companies just
get our data is colonized our everyday
life
and we don't get anything in exchange
so what i realized when we problematized
this problem is that
these critiques are actually quite high
level
they they get this inequality is an
abstract level no
empirics related so little is known
about what is the actual
data science practices and they are
aligned with how they are being
portrayed they are the
they understand what the data science is
doing or the ai creators doing
so that's why i came up with this
question research question
preliminary who says how does the
practice of data scientists compare
with the critics or critiques about data
science or data science
for a research setup is pretty standard
uh i made it the first stage
the research in progress between may and
july
uh due to the current uh
you know current virus thing i had to do
semi-structural interviews
and i didn't have the opportunity to do
no graphic research due to
the situation but i did several
semi-structured interviews with data
related experts with a lot of
from a lot of organizations different
companies i use snowball
sampling and it's important to mention
that i am going to do the number of
research which
should come actually from tomorrow i'm
going to have a meeting with this
company
uh and uh after that i
made it just call it qualitative
analysis
using this software called atlas ti
quite something that
people from design is used to i'm going
to present you some of my preliminary
insights
uh well it seems that some interviews
sure that there's
this start acknowledging the limitations
of
the representation the world
representing the world solely with data
so there is an uncertainty there there
is no only perfect rationality
perspective there is something that they
cannot
account for uh i like this example
for quote that i get from um that i got
from an interviewee when he mentioned
that we can never model all the
information in the world in a computer
algorithm so there is
it doesn't matter how do you do it there
is something always there
in the real world that doesn't let you
get to that perfect
perspective of perfect rationality
uh another important insight that we get
that i got dude is that contrary to what
is criticized for example
in reefs or slaughtering company super
as well
data science is not it seems to engage
in knowledge sharing
and exchanging practices they are not
they do not only
get get other discipline knowledge
just to apply data science to it and
make artificial intelligence systems
with it but they also share their
knowledge of data science and share that
knowledge what they got
through communities of practice and
chapters in organizations and also
through these type of communities
outside of the organization
which is quite interesting uh another
insight that we got is that there is
this emergence of the analytics
translate
analytics translator role which is a
popular role nowadays you can see this
used a lot in silicon valley this is a
similar result of uh
wardenberg and others this is a
colleague of mine
uh that regarding the information
officer so she did a demographic study
in the dutch police
they are trying to apply a predictive
crime tool
and the end the police needed to create
this information
officer role in order to translate the
results that they have
in this algorithm to the officers in
duty
this what suggests that you know the
data science process the artificial
intelligence process is not as
straightforward as thought uh so it
requires in a way
translation uh something
that we get and finally we got
some concerns about data science being
automated and
commoditized so tools such as
automl uh data scientists seem to be
uh aware of these tools and they seem to
be of the let's say quoting
danger that they represent to their
profession because it might get
commoditized in the end and then
they will they won't get any further in
the future
they even talk about changing of uh
profession to go more to an applied
role machine learning engineer for
example data engineer something that is
really required and not can be
commoditized and what this suggests is
that this profession is not secure as
sometimes it's portrayed by the
literature role in popular media
so uh from my final remarks let's say a
discussion
uh discussion on my paper so our
institute suggests that these data
scientists
seem to be reflective of their own
practices right so they are not just
there by themselves
uh in a company just doing artificial
intelligence being the mean
guy being the evil guy or being the evil
people that are creating this ai to
automate us all
and contrary to what is criticized uh
they appear to be vulnerable as other
professions and also to rely on our
professions more than
we think so it is not this close group
of people that is rising in power
without actually having
to communicate and having to
rely on another profession
so yeah what's next in my research
you might be wondering uh i'm as i told
you i'm going to present this paper
to the uh if jwc 2020
conference next month and i'm going to
start with the ethnographic study of the
organization
machine code which is a recruitment
organization uh for
i'm gonna do that for the next year year
and a half uh
of course because it allows face to face
because it is important for
for to get legitimacy and our results to
get this ethnograph
ethnographic study otherwise it might be
just quite superficial
uh yeah this company is a recruitment
organization that is doing
that's currently developing machine
learning algorithms to optimize their
job matching process
they have a team of data scientists
there a chapter of data scientists there
and they
seem really interested in the study and
just
before we ask today questions just a
small commercial break
this is the upcoming upcoming new book
unfortunately
unfortunately it's only in dutch from
our some of my colleagues
and my promoter of course uh from lauren
wardenberg uh marlene hussman and marlux
actingberg
uh this is a compilation of this
ethnographic
studies some of the demographic studies
that have been done with
this ai context and so if you are
interested
in and of course you speak that uh in
learning more about how you can manage
artificial intelligence in practice uh
you can
get the book it's going to be released
on the 24th of november
and i think that we're working right now
into
their english version for those who know
dutch and are interested in the topic
and that's it um i think that we can
just start with the
q a now
mario thanks very much uh for this part
uh
so uh let me see here because anybody
who has questions uh
please uh click the hand button as you
see
luciano yeah please go ahead nice
thanks mario for the presentation um
i was thinking about what you said about
the
the rationality assumption there and i
have like a
two questions connect to this one is
like
um i think if i understood correctly so
the data science they see it as a like a
and or at the market they see like as an
advantage for themselves this is this
you have this rationality and for this i
ask you
one do they also consider these
when they are coding they are also
assume that their code is also
completely
rational in a way i'm saying
and another thing is that it connects a
little bit to your
the concerns of becoming increasingly
automated
that you mentioned that would that mean
that they should be
less rational in a sense but more
focused like
instead of should they have to let go of
their own current advantage would that
mean something in this direction what do
you think
uh well first thanks for that question
that the first one is well
all of the questions are are great
questions
uh yeah we got in the the first one
i think that that's what we want to find
that's that that's what we want to go
that's why we need to do it in that note
to study uh i i currently do not
know the answer to the first one so i
don't know if they are actually thinking
about that
in rationality we the only thing that we
know is we have
some other examples in the literature
but they have not been focusing on
that part of data scientists themselves
for example there are a lot of
examples in scientists normal scientists
engineers
but not in data sciences that's why i'm
doing this this uh
research so it's quite new so okay that
that was the first one the second one
was
uh yes so the point of this rationality
is not about
um that is not
an advantage but i think that we are i
mean at least with the with the paper
what we're trying to do is to defend
the idea of the critics the critic
system that data science has
like these uh the you know the uh
documentaries and netflix and everything
like that technology sucks we
should just get rid of it and we got to
get their ethics which of course that
that's right but
we think that this this type of postures
are too extreme and they are they don't
cover
everything so uh we think that the
perspective
of the data sciences regarding their
rationality
it's something that is innate in them
comes perhaps from their institution
from how they were educated of course
uh but we think that if we can improve
or we can make them
well we can understand them we can
understand if they believe this this
rationality gives them an advantage
and they truly do while they are coding
and
we can kind of like understand that we
can just
produce something in order to help every
everybody align the best that we can in
a way so
you can manage data scientists under
rationality as an advantage
without getting into these problems
that i mentioned before the ethical
problem the epistemology
all that so yes
i hope that i have answered that part of
the question or yeah yeah it didn't i'm
looking forward to seeing what's what
you're going to
find in your ethnographic studies thank
you very much
thanks thanks so so
hi uh mario thank you for uh for your
presentation it's really fascinating to
have you have you with us today
um and i i do really appreciate the
question that you've posted how do
uh data science practices kind of align
or
how do they relate to the critiques of
it and when i'm thinking of the
critiques that
some of them that you've also listed um
you know it's not just about
and this is this is one one of the
things you see a lot that a lot of
critiques go towards
the data scientists and their what their
role is right but they are of course
oftentimes in organizations where
there's other stakeholders or other
decision makers
that sometimes even force them to do
things that they don't have any
agency over and so my question to you is
like
like is is really the data science test
your object
of study and to what extent is your
objective study also like looking at
these actual power power structures and
power relationships and
and the other thing is like the other
objects you could imagine is like what
are the actual
normative choices and decisions and
trade-offs that are being made and
and the you know the the implications of
the of those
um so you could of course you could
track people you could track power
relationships and power structures or
you could track actual implications and
it's
phd is a short time but i kind of wanted
to challenge you or just like to hear
how you think about
uh scoping your your study at this point
yes so yeah the main interest is to know
more about data scientists
um mostly because there is not
a complete complete account of them so
we don't know what they do actually
because it seems that still be
is still an emerging occupation right
i mean 20 years ago was quite really new
the first
time the term was coined was around 2012
i think
2010 2010 so there are recent
but they are they don't have they
haven't been studied yet
completely right and they're involved as
you mentioned in this structure
from the data science team to the
biggest hurricanes of management
and the idea is to look out for for
that relationship as well but of course
i don't i have only
just four years to do that and well no i
just earned
the first one so i only have three years
to do that on
perhaps also to write my dissertation
but uh
yes i am considering myself like
personally going and taking more like a
critical stance a critical
lens to check of course if these data
scientists are the ones that are making
decisions or not
that's that's why these type of studies
are so important because we're getting
these assumptions of
data scientists are either evil or not
evil but we don't know what they're
doing
or if the managers are the ones that are
taking decisions
and how their practices change how these
decisions change
the data sciences practices when they
realize that the world is not as
uh objective let's say the world is
cannot be modeled with
data that they gather do they need more
data do they
what what what is going on there so so
yeah you're right i'm planning to do
that to go more into that
uh in detail i won't be able to do that
of course i need
a lifetime perhaps what if i could get a
nice
nice research or job after this perhaps
i could just keep studying that but but
yes i'm looking out for that
and uh what we have checked until now
is that as we as i we mentioned with
this analytic
translator role they are quite
disconnected with
between there's this connection between
management and data sciences they speak
they don't speak the same language
although they want to so it's really
weird because everybody wants to
collaborate everybody wants to make this
ai but
they don't know how to speak to each
other so that's something really
interesting and we
can just also uh like test that out with
our
ethnographic study to check if that's
that's the case or
it is it is something else uh yeah but
we aren't track that
on that yeah maybe to give you an
example
uh my own experience as a data scientist
there was and this was in in silicon
valley and this is where companies they
tend to you know i often use the phrase
that you know
fake it till you make it so like you
have you know commercial people
marketing people
um ceo going out to customers and
selling certain technologies and then
the data scientists they get
basically they get um
the ones that i worked with for summer
they got like uh
something to build that wasn't you know
the requirements were just like
not possible right but they were uh they
didn't actually didn't have much power
and they were
sent into like a conference room again
and again
and the ceo was saying your accuracy
numbers have to go up because i promised
them
the customer that you know the accuracy
would be more than 90
that we were like they were like 67 or
something but like if you just look at
the data if you just look at
the possible data collection they could
do they were never gonna get there and i
kind of had to help them
through doing more like rigorous
statistical analysis that
look you know it's not going to work and
and there i've seen kind of a
progression where data science actually
got some more
input at the executive level just to you
know
to make sure that when they go out and
they promise things to
to customers that they don't over
promise um so it might also be
interesting for you to kind of
uh in addition to like longitudinal
studies maybe to
to to ask different companies about how
they have struggled in the past and how
they kind of adjusted their
organizational structure the processes
as a result because i i think what i
what i ran into is probably not um it's
not an anomaly so that's probably a
pretty common experience from the last
let's say five to ten years yeah
exactly i mean that's what we are
experiencing and observing as well
that's why we're doing this
research because if you look at these
these critiques
it's more like a data scientists are
super powerful and artificial
intelligence is
everything and then they are the ones
that understand it so they are the ones
that are not getting their jobs
automated so
you should be a data scientist or data
engineer machine learning engineer
and what you're observing as you
mentioned is that that's not the case
but
you need to report it in a way right you
need to you just
to make people notice that
there is something going on there and
then you can actually make it solve it
we were discussing about as you
mentioned perhaps doing
a couple of ethnographic studies instead
of only just
one with another company different
setting different
uh different also field because if you
think about it there's also this
organization that are technological
organization like technology
they do technology and they're these
normal companies organizations then they
just
apply a little bit of technology into
their normal business model
so those are quite some differences the
one that i am doing the ethnography
model is more like normal organization
regular organization that is trying to
apply data science and
implement distribution intelligence in
them so i i'm also wondering if
it will be of course as you mentioned a
good idea to go there to go to other
companies to see
how is this how this has happened right
even though it's the same company or not
but but yes you're right i mean that
that's that's what i'm gonna do that's
what i'm trying to do
to look at for the truth or well not the
truth but what i can observe about what
is
happening and why these critiques are so
pushy towards
a uh they are so great when they are not
so that's what we're trying to do
because we don't have a clear uh image
clear picture
of who is these people behind yeah
yeah let me just say that i've i have
tremendous respect for ethnographic
scholars who go and do this work for
longer times and i
i've experienced myself not not
necessarily in delves but in like
engineering spaces i've been in that
people don't
quickly like realize or like acknowledge
the value
as as i would like them to and who
really i think it's really
it could really be very valuable um
knowledge that can feed back into the
actual way
like you said like the way that we we
educate
engineers data scientists uh
statisticians so
thanks for uh taking on this
challenge
yeah well thank you for uh your
questions now like
make me think thanks
thanks rule uh so while uh please uh
while others uh
yeah i think if you would like to ask
any additional questions and raise your
hand so well
while i wait for you to raise your hand
i will ask a question
i'm curious mario um so this is my
question is somewhere i think
uh kind of on the crossroads of the
objective rationality
points and what you said about the limit
the awareness of data scientists of
limitations of
data modeling of the world so
a lot of the techniques used in data
science these days
they are they they are um
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um they consist of correlation based
modeling
right between correlations between
inputs and outputs
but that process of modeling it's very
different
from a scientific process in which
we make clear assumptions about the
world
around us there and and we have a debate
about
the causality that is at the heart right
as we know correlation
and causality are not the same yeah and
and
um so i'm curious from what you have
seen so far
or perhaps maybe from things that you
are planning to investigate
in your if you're in your research like
is this something that has come up is
there something that you
are planning to touch upon like are data
scientists
aware of uh what are the implications of
that because especially i think
kind of my motivation for asking this
just to be clear is that
in many of the socio-ethical issues that
we
observe with how artificial intelligence
is applied
this is one of the things that really
come up because
we see how certain judgments are made
based on
recommendations that ai systems do that
when you start opening the hood and you
ask well hang on okay
i see that there's a correlation in data
but
from a causal point of view is this
epistemologically like
does it make sense to make such a such
an inference
so yeah i'm curious to hear what you
think about that yeah i mean
thanks for your question jenny's really
nice question i
i i don't know if i will touch it in the
future but i've been
i made kind of like a mini research
before we'll get into literature review
and what we realized is that uh you're
right these
objectivity ideas in inside uh
the development of artificial
intelligence that come from
uh the statistical uh correlation
uh can be also involved in like general
terms
uh you know when we when artificial
intelligence first came up
it was more like a symbolic approach
right try to
imitate the world completely no only
statistical correlations were not enough
but then
machine learning came and then from
machine learning
there was also this
branch of okay because much the original
the original of
the objective of machine learning was to
do it to do the
the same our symbolic artificial
intelligence but
with a different approach but then it
came this statistical approach and then
the statistical approach
just got spread towards everyone so what
we realized is that
the problem around still there
it doesn't matter if it's ai's symbolic
or ai
machine learning uh but because for
us the the the main factor of this
objectivity is that
knowledge is perceived as something that
can be accumulated
so as you mentioned you can say like oh
this correlation
doesn't make sense right and that part
of doesn't make sense is related to
knowledge when it is
a process knowledge
in the philosophical perspective could
be either
something that you can accumulate but
also a process of uh
interaction between people you share it
you use dude you move it around
and that's the thing that we believe
that is missing in artificial
intelligence
and it's research they're so focused on
the idea of
getting all this knowledge that you can
accumulate that instead of
the hard part which is focusing on
the symbolic part and you know the
expert
systems and we at least i understand why
is the reason of that
you know making symbolic efficient
intelligence is way harder
not as efficient as doing machine
learning but there are other ways of
doing
artificial intelligence that for example
a hybrid
uh perspective they are working in the
vu with this hybrid perspective of
joining machine learning with
expert systems or symbolic artificial
intelligence but the point that we
we find it fascinating is this this
perception of knowledge
as can be accumulated or not even
accumulated because it's not even
it's not a symbolic perspective anymore
but
inferred from incomplete data
let's say that there is not this
uncertainty taken
into consideration so we believe that
that's kind of like
the issue with machine learning the
uncertainty part the okay eighty percent
eighty percent is enough but
if you think about a knowledge as a
process eighty percent of your spreading
knowledge is not the same
so there is still there
uncertainty part that is hard to to take
into consideration
uh and also this uncertainty rises up if
you included the biased uh socio-ethical
part or other things that might happen
the
poor explainability so you need to get
over the trade-offs so you're going to
push the technology
and but what we've what we found is that
data scientists seem to be aware
that this is happening aware that
perhaps their definitions of knowledge
are not complete it's not they're wrong
it's just simply it's
not the whole picture and that and that
this is the
perhaps they have they are they are
starting to reflect upon it this is the
right way or as you mentioned
this is correlation doesn't make any
sense but also we have
oh sorry so also the
the management perspective where are
they positioning the company so
a lot of factors there but what i'm
trying to focus in is in that
that perspective that they have and how
they are actually
starting to realize that it is not as
everything is not as beautiful as put it
like in a beautiful correlation of more
than 0.05
and then so and then following up and
you mentioned now the management
so now let me flip it to the perspective
of the
other people in the organization
surrounding the data scientists
so you're saying that data scientists
begin to
acknowledge this more the the this issue
yeah of indeed the knowledge how it's
constructed and the correlation versus
causality and the
[Music]
but what about uh indeed what about the
management the people around the data
scientists so
because one of the things that we
observed there is the narrative uh right
i think i've heard
marlene say that herself in one uh
in the workshop that she's done kind of
the narrative of uh ai
knows better right because there is this
common narrative around us in the world
that we live that
well because ai is quantitative because
it's automatic
people get the perception or it's often
marketed
sometimes even aggressively kind of
marketed as well this is objective and
you know great um
what do you see from the side of the
people surrounding the data scientists
again in the context of this
topic um yeah
what we see is that i
think they they need to give
to perform in their organizations and
that's why they are going to do whatever
it takes to perform
i guess but in the literature of
management you can find that managers
are aware of different types of
let's say knowledge in a way so managers
are reflecting themselves they are
always changing that's why they change
their management styles
but at the same time you are right we we
are looking at
kind of like a process that is
incomplete
even between managers and data
scientists
so there's this knowledge perspective of
they are not interacting correctly
because they they want to but they don't
know how to
right so and even we got sold the idea
that ai is so great
data scientists know that there are some
cases in which ai won't solve their
problems but they don't know how to
communicate this
uh one of the uh as mentioned in the
last question one of the
things that they might do is just rise
in the heritage of the company so they
can have their decisions
heard but at the same time you realize
that
this might also cause some problems not
the idea that they rise into the
hierarchy but the idea that they
are not able to communicate properly
with this management section
about what are the capabilities so in
the end these managers accept the
technology which fails
and then they are not performing
correctly because of this problem
so it's a problem that involves everyone
in the organization
but we we want to go there and to reveal
the truth
what to what extent does it affect who
is
this that the people more that are more
affected with this and what are their
practices
what is happening around in my
perspective i i believe that this is
complete
yeah uh there's a wall between
two of them and is for what i got in the
interviews
the data sciences they they are willing
to to share
even some managers so everyone is
willing to
create the perfect product to do
everything for the company
but yeah again still this infer
interference between
each other of knowledge and their
concepts and epistemologies in which
things go around and what i'm trying to
do to find is how this changes the
data sciences perspective and practices
because they might have started this
data science profession as they are
superior because we are objective but
little by little they are realized that
perhaps they are not as
objective or perhaps there is something
there is a problem they have been taken
by granted or they have been exaggerated
their own
results just for the sake of the company
and that's why we have been observing
if you go to deep into these
organizations that's what i want to
study my phd how this
hype of ai has changed their own
practice
and with this we i expect that you can
just we can create a little
something to help them go through the
company and
avoid these problems because it affects
everyone even
them or the managers if something goes
wrong because of these
problems yeah and i think this is this
is very very
valuable knowledge to accumulate in
order to
like in terms of the kind of questions
for example that we at ai tech and
to delve that often we deal with in
terms of design uh
which you very well know of course
better than me
uh that to to understand uh
how how can we uh steer things towards
the kind of future that we want to see
it's so important to understand the
existing practices
in order that we know how to enter
the conversation uh to be able to steer
things effectively
yeah let me uh let me see does anybody
else have
some additional questions
no hands so far
i i can ask uh probably some more
questions
i mean like related to what you
mentioned about going
there because we need to understand it
uh the way that i see it is that
uh going through the academia and from a
technical perspective
because i have a bad background in
mechanical engineering
uh following through design and then
ending up in social sciences
i think that i can just check out these
problems firsthand by myself
and i can see a difference in
perspective and i think that as you
mentioned it's really important
to understand the why a problem be
before starting to
work and how to solve it right otherwise
it's just a technical problem of i'm
just trying to solve a problem that
doesn't exist
that that just i don't know why this
happens but i'm just applying ai to
everything that i know because of course
we are in
engineers in a way you are curious to
create things to to manage all these
things
but as you mentioned this is really
important as a like
pre-step preface of the applied research
of how do you solve it once you
understand this these practices and how
these practices
have changed in the certain
amount of time
yes
yeah and um let me see it um
so an additional question you mentioned
that um
at one of your slides you said so the
data scientist
might not be as secure as portrayed
um so can you elaborate on that i mean i
think you
maybe you've touched upon this but can
you talk a bit more about that point
yeah yeah i mean i think i can go back
to that one here
yeah um yeah
so the point is that we always have this
narrative this uh aggressive narrative
of you should be
data scientists because or machine
learning engineers something related to
the ai because that's the future
of work and we have always had that
even though before in the past when you
have it professionals
and the internet and everybody's going
to be automated
it's still going and going but the point
here is that
uh you what happens when you automate
the automators
right so when they feel transcendent
because they are disappearing
and that's also important to to to
acknowledge because they might
behave in a different way they might
change their practices in order to not
get extincted so that that that's
something interesting that we we would
like to
to go further because it is not as
secure as it was portrayed so you always
go to the tech world you're never going
to get
your job taken by robots because you are
the ones that are making rods
and stuff but then you realize that
that's not the case specifically for at
least the data science practice
although we believe of course yeah it
might be the case if you don't use
tools like automl and other
smart tools for making data science work
yeah again this is
similar to this phenomenon of uh web
developers
and what happened with uh website
creators you know wix
and all these uh other pages that they
created there so the web developers
became a little bit redundant but in the
end it just
changed a little bit their job and
that's that they switch
to something different to give a bigger
better experience
rather than only just the same just
securing servers and
making sure that the page was online all
the time and available and accessible
so that's that's what i meant with this
is just like things are getting
automated and commoditized so they're
feeling threatened and they
they kind of like they know they have
assumed that that happened they
they they have reflected or at least the
data scientists that i
spoke to they are they they understand
that and some of them are thinking about
changing from data science to
another another field some of the
machine learning engineers they are
planning to stay in the machine learning
engineer field and not going back to the
data science because they also believe
that it's
gonna change a little bit i even have an
interesting response from one of my
interviewees
he believes that the data scientists
will
go will encounter will re-encounter
their way through
software development because they have
been treated as
a shiny little new object you know
doctor
phd is being hired in organizations they
are really important
but in the future they might get into
the same
level as any software development
occupation profession
and that's what they some some of them
they fear that some of them they
just accepted that but there are some of
their
perspective insights and this is
interesting to know how this changes
over time yeah and
so let me ask something that is kind of
yeah
it kind of goes beyond the scope of what
you talk about here but
you you have an interesting journey and
experience yourself as you mentioned
you've in terms of the uh environments
in which you've studied and worked you
had engineering you had design
you know social science when you think
about the way
uh we educate uh how our education works
these days how we educate
ai professionals let me put it like this
broadly kind of yeah
what do you think what kind of
set of multidisciplinary knowledge
should young professionals that graduate
these days
who will mainly focus on technical
things but what kind of
multidisciplinary knowledge do you think
this technical professional should have
in order to work effectively
in the real world i don't want to sell
the sound sorry i don't want to sound
like a
little exaggerated but i think that
design education
will work design is is
actually that i notice in in my
transition to the engineering world to
to these organizations you just
listen to their problems what these data
scientists are experiencing and
there are so many gaps that designers
can feel
in there like absolutely like i think
that design education in any
part of any program around the world in
computer science something like that
i believe that of course etiga ethics of
uh the profession
is a must but a part of design education
of how do you solve problems thinking
out
out of the box how do you how do you
become that bridge
between one discipline and another i
think that that's actually the greatest
asset that's i believe that that's what
helped me with this transition and that
i can just understand this data science
is
better uh and i translated that the
technicalities to the business side or
the social side
so yeah long live design education sorry
yeah that really yeah that really
resonates with me because i've been
thinking the past
two years that i'm working on on the
things that i'm working on i've been
thinking
i wish that back in the day as a
technical student
i had exposure to design just to some
basic
notions and i yeah so definitely
resonates with me
i think probably with more people in the
room and uh yeah i i would love to see
us uh
incorporating some uh basic design
education for our technical students
uh indeed yeah let me see uh does
anybody else have
kenny sorry i fully agree with that
catholic would you like to add something
more yeah i just
said that i fully agree with that yeah
uh yeah does anybody uh cut the line do
you have by the way any
any questions you would like to to raise
or any any more discussion points
it was very interesting uh conversation
i i like it a lot um
i think you're touching upon the
important questions in these
situations and also in relation to
industry i find that
so important and also you know wonder
to what extent we can improve our
education of
our students will be prepared for these
kind of
matters and their responsibility you
know we give them the diploma and we
state
explicitly we point out to them also you
know that
they have a responsibility to society
to um to act according to what they've
been taught
about of course the technology but also
the ethics
yeah are they prepared by just stating
to them that they have the
responsibility can they act on it
difficult
yeah yeah i agree completely
with you i think that perhaps adding a
little bit more of this design education
to all
could help and could improve their
perspective into
not only uh an advice of be responsible
with this degree but also
and to know how to communicate with
others uh and also had to work
together with others uh and knowing how
to
react and how to recognize when
something is not
as well as expected and how to explore
other fields
i think that that absolutely necessary
what i found
these gaps are they are there i think
that there are not enough designers to
fill in these gaps
that i am finding and observing
yeah and it's very much about bringing
the different pieces of the puzzle
together right
i i i say these days like when i talk
about this i say i don't expect
uh computer scientists to be experts in
qualitative research in conducting
interviews
but i would love them and i speak as
somebody who yeah
graduated from technical studies i would
expect myself to appreciate the
importance of working together
with people who can study the context
and can conduct the ethnographic
research and
conduct the interviews and do these kind
of things because together
puzzle and understand what to do
yep any more uh any more questions we
have a couple
a couple more minutes left
so well so let me okay let me then so
maybe i'll finish by
like a provocative question you know but
i want to throw it out there i'm curious
so
the data scientists right and that that
will connect
to what i was saying before about that
if you think about
sciences like in the more uh like
sciences like uh
physics for example in chemistry right
again but where we make some very
explicit assumptions about the world
and then we construct the knowledge
based on the system so again this will
come back to the symbolic versus
correlation
so being very provocative does uh
does the term science and the data is it
appropriate or are we actually talking
more
about without being disrespectful or
anything are we
is it more appropriate to talk about
sort of indeed machine learning
engineering
um you know machine like data anal
analyst yeah
yeah i that's a great question i uh
uh i don't know but uh i i believe that
what i found in the literature and
around in my research
um this is an ill-defined occupation
right so calling it the analysts
calling it machine learning engineer
data scientists um
the point is that what happened was
a started to become more applied
and what that happens perhaps you change
from
physics to mechanical engineering or
physics to civil engineering
uh this might happen with statistics and
you change to statistics to data science
right the the only difference is that
one works with applied
uh data uh the applied world and the
other one
can give the luxury of uh
getting into more uh theoretical stance
when you don't need
actually real world data to to explore
what you do so i think that
it might be depending on the
etymology of the word but i i just want
to say that i i believe that it should
be called different
that then statistics should be more like
applied but applied statistics sounds
too dull
so i think that also data scientists
the term data science and the role of
data scientists
might have been also with these
uh marketing uh objectives behind
because if i remember correctly it was
proposed first by
facebook and linkedin to describe their
uh data analyst uh
professionals and their groups of how
they can handle data
so and it was coined as i i told you
like 2012
2012 so i think that is still emerging
uh still ill-defined occupation um kind
of like in the middle
more or less like design uh
you don't know what is designed right
nowadays it is designed for interaction
this is strategic design business design
engineering design
product design so so i think that they
are in right now in the middle the only
difference is that they are
under the spotlight right design still
behind curtains
shouldn't be there but well still behind
curtains a little bit but as they are in
the spotlight they are
getting a lot of attention so i think
that i
align with what some of my interviews
said uh regarding the future of it
when they stop being the new shiny
object and just
recall their place uh they even got this
new nice phrase of like when they
reached urbana or something and they
just finally get into the position of
all the other ones their software
development occupations
professions and stop being this new
shiny object
uh which is uh perhaps affecting them
now nowadays they're affecting more
because now they already got the
legitimacy that they wanted
it's failed but now things are getting
more more complicated with this social
ethical problems with that are caused by
their creations so
yeah i hope i have answered your
question
yeah yeah thanks very much yeah thanks
very much for sharing that
so last opportunity we have uh for
questions
anybody can i can i may be uh responding
yeah yeah so because i i
kind of agree with a lot of you're
saying mario but it is a very
provocative question
so let me just uh respond to one thing
you said which is that
you know making the analogy of physics
going to mechanical engineering
statistics going to like being a more
theoretical display going towards data
science
let's remember that statistics is the
field
that is totally built on sampling
procedures
that are designed uh that were designed
to make sure that that we could get as
much
like valuable information out of as as
few samples as possible
and so it's it's it's like an inherently
deeply empirical kind of field of course
physics also has its empirical size but
i think even more so like statistics has
really
always been there to serve empirical
studies
um and so i think the way i look
what i the way i look at it is that in
data science
recently there we've been kind of
ignoring a lot of that um statistical
like the
thinking about data and that way because
we you know we've often
have been given like big data sets and
then we make
and i'm generalizing here right but like
we're making assumptions that the data
is fine let's just see what we can get
out of that data
um and through that we're not actually
asking a lot of really important
questions about the limitations of the
data
that might then perhaps lead to like
negative
implications that we then have to solve
with ethics and other
you know design studies to to clean
things up right
that's kind of a way that i look at it
so i think
maybe we should go back actually to
statistics and to understand and that's
hard right this is it's hard to build
a workforce that has those kind of
skills and it will also inherently
um have us meet the limitations of what
data science can do in like
especially in more sensitive domains so
i'm i just want to respond to that i'm
curious what you think about that
and i know we're running out of time but
well i know very quickly i think that
you're right i mean
it might be hard for them because uh
yeah now that they
already obtained the legitimacy but they
are different from statistics
yeah i mean you're right like going
through like
technicalities of what a statistic is
this
is hard uh for them to understand that
they might
still be doing statistics right uh but
that but then you i agree with you
completely agree that there's something
i think that they they are doing
right now touching upon that is that
they are reflecting on that topic
that's why i'm doing the phd and uh or
perhaps i am assuming but i've noticed
that this reflection might have to do
also with
that might end up in that it could be
or not that you can just join data
software engineering as part of that
applied
side and just leave these statistical
assumptions behind
but but i mean i agree with you i think
that it's a great exercise that we just
reflect and that we problematize and
question
why why these things are the way they
are in a way
statistics as the analysis that you have
made just like physics and
my example and the point is
right now is how to understand whether
this field
might go through if you say oh yeah you
should go to statistics again
how they are how do they perceive their
practices which is really important
because even though
we want them to come back to statistics
they have the final
word because yeah they are now they are
a legitimate field
everybody in the world is looking for
data scientists or people that can
create artificial intelligence
but yeah we must wonder what's what
would happen in the future and
i will be there doing ethnography in an
organization so
perhaps when i when i finish that third
year that
nobody knows what happens there i can
just keep
keep keep up and then keep telling
everyone oh i found this i found that
perhaps they might go back to statistics
we don't know oh i don't know yet
i need to get there mario i need to go
but
thank you very much until we meet again
bye-bye
thank you mario mario thanks very much
it's uh
thank you very much for a really
interesting uh talk and a really great
discussion yeah i think uh it will be
great to have you coming back uh then
later on in your research share with us
what you what you find out
uh down the road so thanks very much for
uh
joining us today and uh thank you
everyone uh who participated for joining
and we see you at uh one of our next
meetings
stay safe