Extending Value Sensitive Design To Artificial Intelligence (Steven Umbrello)

uh inviting me i guess
if um given that it's been recorded
would you share that later
yeah um okay
sorry just getting these notifications
from teams
okay uh so to begin the presentation
that i prepared today
um is based on research that i've been
conducting over the last year with evil
vanderbilt
uh we noticed that despite the
continually growing litanies
of ai codes ethics guidelines principles
there's little being done at least
recently
on bridging this theory practice gap
so our aim was to find a nexus in
research
and use the value sensitive design
approach as a methodology for
translating these more abstract
philosophical principles into practice
so past research has explored on how vsd
i'll just keep using vsd just for
brevity's sake to refer to
value sensitive design which i'll
explain later can be applied to specific
technologies such as
energy systems mobile phone usage
architecture projects manufacturing
augmented reality just to name a few it
has similarly
been proposed as a suitable design
framework for even future technologies
both in near and long term
examples include exploratory
applications to nanopharmaceuticals
molecular manufacturing
intelligent agent systems and less
futuristically
autonomous vehicles and although these
studies do provide a useful theoretical
basis for how bst can be applied
to specific technologies they don't
account for the unique ethical and
technical issues
that various ai systems present
and there is ample discussion about the
risks benefits
uh and impacts of a.i although the exact
effects of ai on society
are not neither clear nor certain but
what is beyond
doubt is that a.i is and will continue
to have a
profound impact on human flourishing
more
broadly construed and several scholars
have already explored the ethical
concerns and values necessary to
construct ai
towards socially bad beneficial ends
ai is a nebulous term and it's often
used haphazardly
our use of the term ai then is
understood as the class of technologies
that are autonomous
interactive adaptive and carry out
human-like
tasks in particular we're interested in
ai
technologies that are based on machine
learning which allows such technologies
to learn on the basis of interaction
with
and feedback from its environment these
learning capabilities
pose as we aim to argue specific
challenges for value sensitive design in
particular
ai technologies that are more likely
than not to acquire features that were
neither foreseen
nor intended by their designers and in
addition to the way they learn and
evolve
may be opaque to humans in order to
address these challenges
we suggested that a set of ai specific
design principles need to be added to
value sensitive design
we propose to build on the significant
headway that has
recently been made in the numerous ai
for social goods ai
for sg the abbreviation
as in the title of my presentation
um which are project um
so um this the practical on the ground
applications of ai for social good
factors are already
enacted for various ai enable
technologies and this provides research
with a solid groundwork for how
ethics manifests itself in practice
however ai for social good
is difficult and its underlying
principles are still fuzzy
given the multiplicity of research
domains practices
and design programs yet some work has
already been done to narrow down the
essential ai for social good factors
so to clarify what i want to propose
here today is that value
sensitive design provides a principled
approach that diverse design teams can
adopt
regardless of domain to formalize their
approach to design
ai for social good along these factors
although other tools for achieving
responsible research and innovation
have been proposed vst in particular is
chosen as the design methodology because
of its inherent
self reflexivity and its emphasis on
engaging with both direct
and indirect stakeholders as a
fundamental part of the design process
and the philosophical
investigation of values so i structured
my talk into roughly six main parts
i'm not sure if i okay so i just i'll
switch to the value sense of design one
here in the first part i will lay out
the value sensitive design framework
albeit i'm sure most of uh those who are
listening
uh at least have a running familiarity
with it
so i'll only do so briefly uh the second
section describes
why it is challenging to apply value
sensitive design to
artificial intelligence in the third
part i outlined the motivations and
descriptions of the ai for social good
factors as a way to address the specific
challenges raised by ai for vsd
in section four i'll outline a design
approach inspired by value sensitive
design and the ai for social
good factors illustrating an organic
symbiosis of the two
uh and in the final section i use the
example of a specific contact tracing
app to provide a preliminary
illustration of the approach
um and then i'll close with some summary
and some concluding thoughts i think
ideally if anybody wants to ask
questions given that i've divided
this into five six sections maybe we can
do so
at the end of each section uh and then
those questions could just uh be
communicated to me before we move on to
the next section so
value sensitive value sensor design
is a often construed as a principled
approach to take values of ethical
importance into account
in the design of new technologies the
original approach was
developed by patya friedman and
colleagues from the university of
washington
but the approach is now more widely
adopted and has been developed further
by others sometimes
under somewhat different headings like
values at play or adelph
designed for values at the core of the
vsd
approach is what uh friedman company
called the tripartite methodologies you
can see here
of empirical conceptual and technical
investigations
these investigations can be carried out
consecutively in parallel
iterably or iteratively and they involve
one empirically investigating the
relevant stakeholders
their values and their value things and
priorities
sorry uh uh
just as a note i'm not able to see what
the next slide is
uh in microsoft teams so that may be a
future note unlike
uh screen sharing the powerpoint
directly so i can't
anticipate the next slide so just
forgive me if i just go back and forth
quickly
so these this iterative uh
investigations involve one
empirically investigating the relevant
stakeholders their values and value
understandings and priorities
secondly conceptual investigations into
values and possible value trade-offs
or moral overload and three technical
investigations into value
issues raised by current technology and
possible
implementation of values into new
designs
friedman and hendry for example propose
17
more specific methods that can be used
in value sensitive design ranging from
stakeholder analyses and tokens
to value-oriented mock-ups and
multi-lifespan
code design one important issue in vsd
is how to identify the values that
should be taken into account
in a concrete vst process freedom and
company propose
a list of 13 values
that are important for the design of
information systems like human welfare
ownership and property privacy and
freedom from bias among
others others have proposed such an
approach and argue that is better to
elicit values
bottom up from stakeholders both
approaches probably have their
advantages
and disadvantages a value list may
well miss out on values that are
important in a specific situation
but are not on the list although
bottom-up elicitation may help to
discover such values
it's also not watertight as important
values may not be articulated by the
stakeholders
or crucial stakeholders may not have
even been identified
moreover not every value held by
stakeholder is a value of ethical
importance that even should be included
in value sensitive design
for the case of ai some considerations
are important when it comes to
identifying values
in the vst design process of ai
technologies
first there is now widespread consensus
that
ai raises specific specific ethical
issues
which are not or at least to a much
lesser degree
raised by more conventional uh
information
communication technologies this has two
implications for
the issue of value identification first
the original vsd
list of values does not suffice for ai
instead one may for example take the
values
identified by the high level expert
group
uh on the ethics of ai as a starting
point so
respect for human autonomy prevention of
harm
fairness and explicability secondly
some value list would seem desirable for
the case of ai to ensure that
the typical ethical concerns that arise
from ai
are not overlooked this is not to say
that no other value should be included
in the design of ai applications they
should and
some form of bottom-up elicitation may
be relevant here
but it should be supplemented by
principles that ensure that typical
ai ethical issues are properly addressed
and
we propose to have recourse to the ai
for social good factors
that i'll discuss more in the third
session
uh section so the challenges
posed by artificial intelligence to
value sensitive design ai applications
pose
some specific challenges when it comes
to bsd more generally and this is
particularly due
to the self-learning capabilities of ai
this complicates the reliable
integration of values
in the design of technologies that
employ artificial intelligence
so we can use a short imaginary but
illustrative example
and then we can discuss more general
terms the complications raised by ai for
value sensitive design
so suppose a tax department of a certain
country wants to develop an
algorithm that helps to detect potential
cases of fraud
more specifically the application should
help civil servants to select those
citizens
whose tax declaration needs extra or
special scrutiny now suppose they choose
to build a self-learning
artificial neural net for this task like
you can see here
an artificial neural network consists of
a number of input units
hidden units and one or more output
units
so let's suppose that the output unit or
variable is simply a yes no
indicating whether specific tax
declaration
needs additional scrutiny the input
variables or units
can be many including for example the
amount of tax to be paid by a certain
citizen
the use of a specific taxes exemption
prior history of the person
for example suspicion a fraud in the
past
but also personal details like age sex
place of living
etc the units or variables in the
artificial neural
network are connected as you can see in
the figure the connection between the
units can be
weight factors that are learned by the
algorithm
this learning can be supervised or not
if
supervised learning is applied if
supervised learning is applied
the algorithm may learn to make calls on
what tax declarations need to be
scrutinized that are similar to those of
experienced civil servants at the tax
office
in the case of unsupervised learning
information on which scrutinized cases
lead to detection of actual fraud may be
fed back into the algorithm and it may
be programmed to learn to select those
cases
that have the highest probability of
leading to the detection of actual fraud
now one of the values that is obviously
important
in the design of such an algorithm is
freedom from bias
this value is already included in the
original list of value sensitive design
values proposed by
friedman and khan back in 2002
and freedom and nissenbaum defined
freedom from bias
in reference to computer systems that
systematically
and unfairly discriminate against
certain individuals
or groups of individuals in favor of
others i'm not sure if i wrote that here
no
uh in traditional vsd this may be
implemented in the design of the
algorithm in a number of ways
first and foremost it may be translated
into design requirements
that none of the variables in the
artificial neural network
the nodes in this figure
uses such variables may lead to unwanted
bias
for example ethnicity may be ruled out
as a potential variable
however this will not be enough to
ensure the realization
of the value freedom from bias as bias
may also be introduced through proxy
variables for example postal codes
may be used as a proxy variable for
ethnicity
and one may also want to rule out the
use of such variables to ensure
freedom from bias but even then a
self-learning algorithm may be biased
due to the way it learns it may for
example be biased
because the training set for the
algorithm is not representative or
execute
if a form of supervised learning is
chosen it's conceivable
that the algorithm learns from the bias
that was already in human judgments
that are already used for the supervised
learning
but even if these potential sources of
bias have been excluded
it can't be guaranteed that the
resulting algorithm is not biased
certainly not if a form of
non-supervised reinforcement learning is
chosen
one issue is that the resulting
artificial neural network may be
described as following a certain rule
even if the rule was never encoded nor
can be easily derived
from the variables in the artificial
neural network
in other words it is conceivable that
the resulting algorithm can be described
as following a rule
that is somehow biased without the
result being foreseeable
or even clearly discernible this means
that bias in the algorithm in this
imaginary at least
may be emergent and opaque emergent
in the sense that it is an unintended
and unforeseen consequence from the way
the algorithm has learned opaque in the
sense that it may be
it may not be immediately clear for
humans from
inspection of the algorithm or
artificial neural network that it
is biased the point is more general and
doesn't just apply to the specific
example
or the value of freedom from bias or
fairness
due to their self-learning capabilities
ai systems in particular those powered
by machine learning may develop features
that were never intended nor foreseen or
not foreseeable by their designers this
is also uh this also means that they may
have
unintended value consequences and it can
even imply that they
unintentionally disembodied values that
were
embedded in their original design
moreover
these unintended features may not always
be discernible
as they may be due to specific ways the
algorithm has developed itself
that are hard or even impossible for
humans to fully understand
the important point is that addressing
emergence and opaqueness
requires a set of design principles or
rather design norms
that are not needed for traditional
technologies
is there a reason oh i'm not sure why my
slides were
automatically moving through sorry
uh so some of these principles related
to
the technical or design requirements
others relate to the organization of the
design process and the further life
cycle of a product
like continued monitoring and still
others may have to do with what ai
techniques are being used or not so
we're going to move into the next
section which will look at the proposed
ai for social good factors as a way to
address
the specific challenges that ai poses
for value sensitive design
uh yes okay
so uh the most thorough work on the
harmonization of
the of ai for social good values has
been recently undertaken
by luciano floridi and company at the
oxford internet institute
whose focus on factors that are
particularly relevant
to ai not exhausting the potential list
of relevant factors
the seven factors that are particularly
relevant for the design
uh of ai towards the social good are
as you can see in the figure here
falsifiability
and incremental deployment safeguards
against the manipulation of predictors
receiver contextualized
intervention receiver contextualized
explanation
and transparent purposes privacy
protection and data subject consent
situational fairness and human friendly
semanticization
the seven factors although discussed
separately
naturally co-depend and co-vary with one
another
and are not to be understood as rank
ordered or in a hierarchy
similarly the seven factors each relate
in some way to at least one of the four
ethical principles that the eu high
uh high-level expert group on a.i laizel
so uh respect for human autonomy
prevention of harm fairness and
explicability
this mapping on the more general values
of ethical ai
are not insignificant any divergence
from these more
general values and of ethical ai
has potentially deleterious consequences
what the seven factors are meant to do
then is to specify
these higher order values into more
specific
norms and design requirements uh for the
sake of time i
i forgot the summarizing of the ai for
social good factors
uh floridian company do so quite
succinctly
in uh their new paper published in
science and engineering ethics at the
beginning of the year
so we'll move uh along so adopt the uh
adapting the
value sensitive design approach uh in
order to address the challenges
posed uh for vsd by artificial
intelligence we propose a somewhat
adapted value sensitive design approach
these adaptions that uh these
adaptations
uh we propose are threefold
one is integrating the ai for social
good factors
in value sensitive design as design
norms
from which more specific design
requirements can be derived
secondly distinguishing between values
to be promoted
by design and values to be respected by
design
to ensure that the resulting design not
does not only not
do harm but also contributes to doing
good
and thirdly extending value sensitive
design process to encompass
the whole life cycle of an ai technology
in order to be able to monitor
unintended
value consequences and redesign the
technology if necessary
so we first briefly explain these new
features and then i'll sketch
the the overall process so
integrating ai for social good
principles we propose that
to map the ai for social good factors
onto the norms category
uh used to translate values into
technical design requirements and vice
versa we use
evil vendor pulse value hierarchy
likewise an entire typology of available
practices and methods for turning
the principles of ai for social good
benefits
beneficence non-maleficence autonomy
justice explicability
as well as case studies based uh have
been gathered by digital catapult
into an applied ai ethics typology
however these methods remain pretty high
level
and are not specifically operationalized
for designing for ai for social good
and for this reason vst is proposed as
an app starting point at the very least
given its theoretical overlap with ai
for social good factors
as norms for translating these values
into design requirements
so distinguishing between values to be
promoted
and values to be respected uh in order
for
the value sensitive design approach um
to ai to be more than just avoiding
harm and actually contributing to social
good
an explicit orientation is required to
socially desirable
ends such an orientation is still
missing in
current proposals of ai for social good
projects
we propose to address this by an
explicit orientation to the sustainable
development goals
as proposed by the united nations as a
best approximation
of what we collectively believe to be
valuable
societal ends in 2015 all the member
states of the united nations adopted the
then proposed 2030 agenda for
sustainable development
a proposal aimed at the design and
implementation of goals towards a safe
and sustainable
future founded on an agreed desire uh
for global peace
the general adoption of the resolution
is towards making
actionable these 17 sustainable
development goals that form its
foundation
it recognizes that the included
sustainable development goals must not
be looked at
as mutually exclusive of one another
rank ordered or as
trade-offs but rather sustainable
development goals
uh such as ending poverty and climate
change remediation go hand in hand
among ending poverty and climate change
action you can see that there's other
goals such as affordable
and clean energy industry innovation and
infrastructure
and sustainable cities and communities
just to name a few
and thirdly in the that the new
tripartite steps
are is extending vst to the entire life
cycle so in order to address the
emergent and
possibly unintended properties that ai
systems acquire as they learn we propose
to extend vst
to the full life cycle of ai
technologies
in order to keep monitoring the
potential
unintended value consequences and
redesign the technology if necessary
a similar idea is voiced in the ai for
social good factor
number one ai for social good designers
should identify falsifiable requirements
and test them
in incremental steps from the lab to the
outside world
the need for ongoing monitoring arises
from uncertainties that accompany new
technologies
that are introduced in society this
adapted approach to vsd
process i we illustrate here
this illustration serves as a general
model
that we hope engineers can then use to
guide them
throughout their design program we
suggest that ai
for a value sensitive design for ai
proceeds in four iterative phases
uh that we that'll briefly describe so
context analysis so motivations for
design differ across different design
projects of course
for this reason there is no normative
starting point that designers
must begin with vsd acknowledges that
technology design can then
begin with a discrete technology itself
as a starting point
uh context of use or a certain value
in all cases an analysis of the context
is crucial
various contextual variables come into
play
that impact the way values are
understood which we'll see in the second
phase
both in conceptual terms as well as in
practice
on account of different social cultural
and political norms
eliciting stakeholders in social
cultural context
is imperative within the vst approach to
determine
whether the explicated values of the
project
faith faithfully map on to those of
stakeholders
both direct and indirect and to that end
empirical investigations play
a key role in determining the potential
boons or
downfalls of any given context and
engaging
with the context situated nuances
of the various values may come to play
in any given system
various pitfalls and constraints can
begin to be envisioned
particularly how the initial core values
can be understood
in terms of technical design
requirements
which is the third phase
so um value identification the second
phase concerns the identification of a
set of values that form the starting
point of the design process
we suggest three main sources of such
values
values that are be promoted by design
for example deriving from the sdgs
formulated by the un
values that should be respected in
particularly those values that have been
identified in relation to ai
respect for human autonomy
non-maleficence
fairness explicability and thirdly
context-specific values
that are identified in the first phase
in particular
the values held by stakeholders it
should be noted that phase two does not
just involve empirical investigations
but it has a distinct
normative flavor to it in the sense that
it results in an identification of
values that are to be upheld
further in design from a normative point
of view
in addition this phase involves
conceptual investigations
geared towards uh and interpreting in
context the conceptualization of
certain values so
third formulating design requirements
the the third phase involves the
formulation of
design requirements on the basis of the
values identified in the previous phase
and the contextual analysis of phase one
here tools like the value hierarchy can
be useful
to mutually relate values and design
requirements
or to translate values into design
requirements
we suggest that the translation of
values into design requirements is
somewhat different for the different
sets of values
that were formulated in the second phase
the first set of values derived for
example from the sustainable development
goals
are values that are to be promoted they
are typically translated
into design requirements that are
formulated as a criteria that should be
achieved as much as possible
the second set of values are those that
need to be respected
in particular in relation to ai here the
ai for social good factors are
particularly helpful to formulate more
specific design
requirements these requirements are more
likely to be formulated as
constraints or boundary conditions
rather than as a criteria that should be
achieved as much as possible
and these boundary conditions set the
the ontological constraints that any
design
needs to meet to be ethically or
minimally
acceptable and for the third set of
contextual values the context analysis
and in particular the stakeholder
analysis will most
likely play an important role in how
these are to be translated into these
design requirements bsd provides a
principled and widely
disseminated approach to aiding
designers
in putting such processes and abstract
values
into technical practice
and finally the fourth phase is the
building of t
and testing of prototypes that meet
those design requirements
the idea is here in line with what is
described more generally in the value
sensitive design approach as
a value-oriented mock-up prototype or
field deployment
aims at the development analysis and
co-design
of mock-ups prototypes and field
deployments to scaffold
the investigation of values value
implications
of technologies that are yet to be built
or widely adopted
our proposal is to extend the space to
the entire life cycle of an ai
technology because
even if such technologies may initially
meet
value-based design requirements they may
develop in such
an unexpected and undesirable effects
that can materialize over time or they
no longer achieve the values for which
they were intended
or they may have unforeseen side effects
which require additional values to be
considered in such
cases there's reason to redesign the
technology and do another
iteration of the cycle and
in order to ensure the uh i guess you
say adoptability
and illustrate the efficacy of this
approach what we do is we provide
a timely example to more clearly show
how the process works by situating it in
a figurative context for a specific
ai system so before i move on to
i believe this is the final part uh on
our the application the illustration of
the application
is there any questions
so thanks steve let's see uh so
does anyone have any questions i don't
see right now uh
hence uh let's see yes yeah
i see that i can okay uh derek uh please
go ahead
ah sure uh thanks really enjoyed so far
um
some uh examples would would be helpful
and
in particular the lower left quadrant
um your minimal
um uh sorry yeah lower right
uh yeah the this middle piece here
um you're talking about uh
so in in the this specific translation
process and the minimally ethical
approach do you have a just a a picture
in mind or
an example system or some um
way to to make this idea concrete
well uh the the section that i'm about
to or where i'm planning to move now is
an application of this entire approach
to uh contact tracing app so um
as i'll explain i'll be using the german
example of a particular concrete
technology
and that'll at least i i ain't used to
help to illustrate this so
okay and then if you have the same
question after if it's not clear then we
can explore that
thanks uh you know yes hi uh
stephen thanks very much inaudible here
um
most of what you explained so far refers
to the design phase of a product or
service right and as you said
step by step slowly move from the lab to
deployment and then
later on and actually just now you also
mentioned that
during the deployment of course changes
might might occur now
um you said you know a certain goal
might not be achieved anymore certain
values might no longer be obeyed
so let us redesign but of course the
power of ai
is not so much that it's self-learning
during the design phase actually i would
not even call that self-learning i would
call that training
the self-learning aspect only exhibits
itself when it is in operation right a
product
in the field being deployed out of the
hands of
the manufacturer the programmer the
legislative body etc so how do you look
at that problem once a product is out
there and does its thing
um how do we you know i mean bring it
back to the lab or to the design
phase is probably out of the question so
how do you look how does that fit in
your storyline let me
let me put the question like that yeah
i'm interested to hear so that's
actually like
extremely interesting and that's the i
guess where some economic values start
to come into
um into some serious tension and this
this i think makes more sense when we're
talking about the difference between
hardware and software of course
when we have hardware deployment a total
recall although
not unprecedented for certain
technologies
uh like certain vehicles is has
a lot of economic barriers that's that's
for sure
it's not unheard of either for
this type of redesign to be more
[Music]
this type of post talk redesign
to be undertaken when we're talking
about the software side so the ability
for designers let's say a company to
roll out either either updates or
put a freeze on a certain firmware
update
i i can see this definitely becoming a
particular issue
um i'm actually unsure do you have any
idea on how that can actually
be undertaken uh if we put aside
the the more the tension with economic
values
uh or what would actually happen in the
real world
but just from a conceptual i guess um
approach
how would this actually be undertaken
undertaken in this sense of i mean i
mean there's this
there are a number of well-known
examples also operational
maybe from limited domains but for
instance there is this company
that trained a medical diagnosis system
in their lab the system was certified
because it had a certain performance
according
to some standards blah blah it was
shipped out but this was a self-learning
system so the opinion of the medical
doctors in the hospital where the
software was installed was then factored
into
further training so this was indeed
self-learning so the system started to
deviate
from the original um you know shipped
product
not in the sense that the code was
different not at all but there was just
different
samples being fed into the system and
therefore let's say the implicit
decision boundaries of the neural net or
decision tree or whatever they were
using
were shifted because of the operation in
the field
now that that already brings questions
of who is responsible yeah so the whole
responsibility of
accountability discussion starts to play
a role but i'm interested to see how
that story then fits in your vsd
um you know design principle
um maybe i'll explain that a little bit
later but in terms
as of right now in terms of the ai for
social good
um factors particularly receiver
contextualized
explanation and transparent purposes
um there has to be
a means by which such systems and maybe
the example you brought up
that that was present is
why does the system do what it does yes
in light of its field deployment
both differently to the users as well as
to the designers is there a way to
track and trace the decision-making
i guess you could say architecture
architecture of the system itself in
order to understand
why it does what it does it's not
necessary and i don't
know if it's necessarily important in
terms of the
the actual token action of the machine
but
the the typology of action uh
as promoting social good or being
constrained
by certain ethical values so it's kind
of
is it more important the car that's on
the road or how the road itself is built
that allows what kind of car on it so
i'm speaking mostly in terms of the
decision of a system based on its inputs
and then
therefore its outputs i would actually
be interested in that particularly
example itself
is what kind of designer intervention
is built into that kind of system
post-deployment uh do does the designers
or the industry that's responsible for
its design
have continual monitoring of such a
system in order to roll out
impromptu updates uh in light
of maybe a recalcitrant or an
unforeseeable
decision that the system made
[Music]
i would actually be interested in that
if maybe later you can send me
the a link to that particular that
particular example
okay steven uh
so just before you move on just one uh i
just wanted to jump in and ask you
what about the design requirements part
i was curious what are your thoughts on
how do we avoid uh technical solutionism
in this part so i mean by that coming
into the design process with a
preconceived notion that
we're already going to solve everything
with ai in this case so how do we
do this truly in a socio-technical
manner in your opinion to recognize that
and i think you kind of refer to this
here because you say it's both process
and product requirements so there's and
i'm curious what are your thoughts about
the process because what are the human
to human interactions organizational
practices
that need to be around how do we open up
our imagination to include that in our
thinking
well i guess one of the main
interventions
is the direct and indirect stakeholder
elicitations which
the value sensitive design approach has
probably more than
five different methodologies for
stakeholder identification
stakeholder elicitation and then their
value identification
translation understanding and analysis
um whether or not those specific
and it technically is not exclusive or
exhaustive
that list that's just the current list
and it's continually being updated
coming from the social sciences
so there are interventions on kind of
breaking open
the bubble of a design program in order
to get these new types of perspective
envisioning cards
is actually a pretty excellent way of
opening up
innovation um particularly from a more
closed off domain
when we when we move into realms like
the military for example military
innovation that's where things start to
get
a little bit more dicey because of its
closed nature
but in terms of the solutionism that
they bring up i think
that the empirical investigations
of particular stakeholder values are
not only important but necessary uh not
only if you want to
uh undertake the value sensitive design
approach because that's one of its
fundamental tenets
but it's done that it's
it's there for a reason it's there for
that i think exact reason it's
moving outside of what would be a
limited domain space
of uh of design thinking
thank you so i see there's more
questions coming but let me first let
you move on with the presentation
and then we see in the time left if we
can yeah so this is just this is the
last section
uh just uh as an example and then i
guess we can we can talk
okay sounds good okay so
um on tuesday april 7th
2020 so this is a slightly outdated uh
the robert
uh cook institute the german federal
research
institute responsible for disease
control
and prevention prompted german citizens
with smartphones and smart watches to
voluntarily share their health data
to keep track of the spread of covet 19.
the rki that robert cook institute is
rolling
out i'm not sure maybe some of you would
know if it has been fully rolled out or
rolled back
um the app called crota that's in spend
the corona data donation which allows
users to voluntarily
and pseudo-anonymously share their
health data to aid scientists in
determining symptoms related
to covenanting infections and its
distribution across the nation
as well as to gauge the efficacy of the
amelioration measures that they put into
place
the app allows users to record their age
height weight
gender metrics such as physical activity
body temperature sleep behavior heart
rate as well as
postal code lothar weather head of the
rki
said that the collected information will
help to better estimate
where and how fast copa 19 is spreading
in germany
and the rki is explicit that the
collected data of individual users
are labeled as pseudony pseudonyms that
the personal information of users such
as names and addresses remain private
through the de-identification of user
data through artificial
identifiers leaving the possibility of
re-identifying data subjects
open likewise the machine learning
systems underlying the app are designed
to
recognize symptoms that are associated
with among other things a coronavirus
infection
and these include for example an
increased
resting heart rate changes in sleep
activity and behavior
and the data donated is said to be only
used for scientific purposes
and after careful preparation the data
flows into a map that visually shows the
spread of potentially infected people
down to the zip code level
and all i believe still in synthesis
regarding the deployment stages
uh keep in mind that when we were doing
this research it was at the beginning
of the outbreak we could still
illustrate the design which is used as
an example for the design
of the corona death and spend app i'll
be at x post facto
in this case using the framework we
that i outlined already and the goal
here is to demonstrate how this modified
vst approach can be adopted
uh to a specific technology and should
not be read as providing the
actual design requirements for the app
uh albeit
still providing some food for thought
for those engaging in
the design of it so um context so
as mentioned vst acknowledges that
technology design can begin with a
discrete technology
as itself as a starting point the
context of use or
a certain value in this case the context
of use can be understood as the
motivating factor behind the
technological solution
simply put the outbreak spread and
eventual declaration
of a global pandemic of copen19 provides
the context of use and development the
immediate health crisis
demands swift action to be taken in
order to stifle
further spreading but also
the desire to return to less strict
measures at some point
uh some point post-pandemic um is also
uh warranted a prima facie analysis of
the values at play here
can be said to be tensions between more
immediate
public health and economic stability and
prosperity
the development of an app can
specifically be targeted at trying to
balance this tension
uh as a tracking and tracing that may
assist in uh resuming
certain social activities like traveling
or work
it's in a way that still reduces health
risks as much as possible
by tracing who is potentially infected
value identification so firstly
values that are to be promoted by the
design and for example those ones that
are deriving from the sustainable
development goals
the design of the coronado spend app can
be said to be part of a large network
to support for example sustainable
development goal three
good health and well-being which aims
among
other sub-objectives to focus on
providing more efficient funding of
health systems
improved sanitation and hygiene
increased access to physicians
and more tips on ways to reduce ambient
pollution
albeit an impromptu technology
introduced as a response to an immediate
context
insito deployment and use may encourage
applications outside the original
context
for example outside of germany and also
perhaps for
other illnesses secondly
values that should be respected uh in
particular those that have been
identified in relation to artificial
intelligence so respect for human
autonomy
uh prevention of harm fairness and
explicability
respect for human autonomy in the
context of ai
systems autonomy refers to the balance
between
the power humans have in making
decisions and how much
of that power is abdicated to those
systems
not only should machines be designed in
such a way as to promote human autonomy
but they should be designed also
to con string the abdication of too much
human decision making power
particularly where such human decision
making outweighs the value
of the efficacy of the machine's
decision-making capability
and this is aligned with sustainable
development goal 16
for example peace justice and strong
institutions particularly
the sub goal of 16.7 ensuring responsive
inclusive participatory and
representative decision making at
all levels prevention of harm
or uh not maleficence is framed as
preventing potential risks and harms
from manifesting themselves and systems
by understanding their capabilities and
limits often
questions of data privacy and security
are evoked
as to how individuals control their
personal data
the rki in germany is explicit that it
does not collect personal user
information
beyond the level of postal codes to
understand transmission densities
however privacy concerns still exist at
the community levels nonetheless
particularly in the practices used to
store use
share archive and destroy collective
data
risks of regional gerrymandering
targeted solicitation or discrimination
are not exclusive are not excluded
solely on account
of delimiting data collection to the
postal code level
harm may occur due to the specific ways
the app is used
particularly if the app is not only used
to
map the spread of the virus but also to
trace individuals
as potential bearers of disease and risk
factors
and i'll discuss that more in the
contextual values
fairness uh which is albeit an ambiguous
one and often described and defined in
different ways and specific
specified across different points in the
life cycle of a.i
and its relation with human beings
fairness can be understood as being
framed as
justice as floridi and uh and company do
at oxford
and they sum up various definitions of
justice in
at least three ways using ai to correct
past
wrongs such as eliminating unfair
discrimination
ensuring that the use of ai creates
benefits that are shared
or at least shareable and finally
preventing the creation of new harms
such as the undermining of existing
social structures
which is directly in line with
sustainable development goal 16
at the very least peace justice and
strong institutions
and finally explicibility uh the
employed ai systems
in order to support the other values
must be explicable
this means that its inner workings must
be intelligible
that means not opaque and there must be
at least
one agent that is accountable for the
way it works and
they understand the way it works and are
thus responsible
for its actions whatever you can define
agent as
an individual group and
finally in value identification
context-specific values that are not
covered by
one or two in particular the values held
by stakeholders
and we refer here to the development of
like the dutch
tracing and tracking app to illustrate
how contextual values
may be relevant for the design of such
an app so like in the netherlands
uh at least at the beginning of uh the
pandemic 60 scientists and experts wrote
the open letter to the dutch government
in which they warned against the number
of risks and unintended effects of
tracing and tracking up i wouldn't doubt
if some of them are here listening
um among other things they pointed out
that such an app may lead to
stigmatization
and discrimination and might depending
on how it would be used
endanger fundamental human rights like
the right of association
and they draw attention to the fact that
the app might give a false sense of
security
which might lead people to no longer
strictly following the requirements for
social distancing which may increase
rather than decrease health risks
and although it was announced by the
german government that corona data spend
would be voluntarily
voluntary scholars also pointed out that
the app might nevertheless be used to
allow access to certain services like
public transport
or might become requirement by their
employer uh by employers for their
employees which would endanger the
voluntariness
of its use such potential uses might in
turn also invite individuals to not
properly use the app in order to keep
maximum freedom of movement
and conceal certain contacts by turning
off the phone for example which again
might contribute to health risks so many
of the risks and potential side effects
mentioned by scholars for
the the coveted 19 apps map onto the
values we already discussed
uh previously in particular health
values under
one and non-maleficence justice
autonomy and explicability under two for
example a false sense of security
relates to the value of health
and privacy involving their
voluntariness to the value of autonomy
while stigmatization and discrimination
relate to fairness
nevertheless there are also values like
the right to association for example
uh security against hacking or misuse
that are less clearly related to one of
those values
although they can perhaps be subsumed
under non-maleficence
nevertheless uh the what the issues
particularly
show is that we should consider values
in context in order to gain a full
awareness
of what is at stake and how to translate
these concerns
into tangible design requirements in
this specific case
it's for example particularly important
what behavioral effects apps will have
and it's also crucial to view the values
in a broader system
in broader systems context in this sense
even if a contextual analysis
may not reveal completely new values it
will nevertheless be crucial
in understanding how values are exactly
at stake
for specific application how these
values are to be understood
in the specific case and how they
translate into
design requirements the third step
as we mentioned above is the actual
formulation of design requirements so to
illustrate how tools like the value
hierarchy can be used to visualize and
aid designers
in translating abstract values from the
technical design requirements
we provide a specific instance of the
tool here and of course this should be
taken as one of
numerous iterations that can occupy any
given vector in the hierarchy
but this is just one example here the
value of non-maleficence was chosen as
the more abstract higher level value
that was then translated through two ai
for social good factors five and six and
then into
technical design requirements in this
paradigm
ai for social good factors are adopted
as norms
and rightly so given that they are
framed as imperatives by floridian
company
naturally any given context of use
value and specific technology will
implicate
a number of combinations and there is no
exclusive nor exhaustive route
for satisfying a value translation and
it can move
in bought in a bottom-up direction also
design requirements the norms the values
as well as
how you sit here as top down from values
to norms to design requirements
uh situa situational fairness for
example could just as easily and
probably should
be used as the normative tool for
operationalizing other values such as
explicability
uh transparent data set collection use
storage as well as justice
which can be understood as promoting
non-discriminatory laws and practices
through unbiased compliance an example
of this
would be the fairness warnings or fair
mammal which have
recently been proposed and at a
functional level the normative structure
of ai for social good norms supports
avoiding most ethical harms
associated with artificial intelligence
systems however they
per se do not guarantee that all new ai
applications will contribute
to the social good the higher level
values that i spoke about
in conjunction with related real
operalization
of the sustainable development goals
allow more salient ai systems to be
developed
that contribute to social good global
beneficence
this multi-tiered approach of coupling
ai
specific values stakeholder values
and their application to sustainable
development goals
attained via ai for social good norms
can mitigate the dangers posed by
ethical whitewashing
that occurs through the legitimization
of ai technologies
that do not respect some fundamental
fundamental ai
principles regarding this type of
visualization
uh can be used across different sources
of values as listed above
such as the sustainable development
goals and stakeholder values to
determine how
accurately related values can produce
both
similar and different technical design
requirements
i think a fruitful future research
project could do this empirically by
taking any particular
ai technology and provide thorough value
to design requirement translations
to determine the effectiveness of this
approach
regardless our aim here is to help
designers to more
effectively design for various values in
mind
ones that are oftentimes erroneously
conflated or
altogether sidelined
and finally prototyping as i briefly
mentioned involves building mock-ups of
the technology in question
according to the design requirements
laid out in the previous step
this means that the technology is moved
from the more controlled space of the
lab or design space and in situ which of
course
implicates direct and indirect
stakeholder values
at this point various design decisions
may prove to be recalcitrant
or unforeseen recalcitrant behavior
emerges that implicates other values at
this point given the technology's
limited deployment it can be recalled
into the design space
so that the corrective modifications can
be implemented
regarding the corona data spend app for
example the crisis situation that
underlies the motivation behind the
app's inception
invites direct deployment rather than
prototyping given the stakes at play and
the
urgency for amelioration although
tempting
this may ultimately be unwise
given that the significant risks that ai
systems possess
particularly once predicated on such
large quantities of data subjects
small-scale deployment or in-house
testing of the efficacy and fidelity of
the app's underlying systems
are necessary although not sufficient
conditions
for responsibly developing an ai system
of this type
to ensure that it can help to achieve
positive ethical and social values like
beneficence justice explicability
and the associated distal sustainable
development goals
while reducing ethical ai risks
non-maleficence what should be
particularly stressed
is that prototyping should not be
restricted to testing the proper
technical functioning of an app but
should be
should take into account behavior as
well as societal effects
and ultimately the effects of these on
values
here the tracking and tracing app is a
case in point while some value issues
like privacy
may be addressed through technical
choices like pseudonymization
local storage of data and automatic
destruction of data after a certain
period of time
some other value concerns require
insight in the behavior
effects of such an app such behavior
effects are very hard if not impossible
to reliably predict without some form of
prototyping
at least small-scale testing in situ it
would therefore be advisable
to go through a number of trials for
such an app that scale up from
very small scale testing with mock-ups
to testing
in test settings of increasing size not
unlike what is
done in medical experiments with new
drugs such testing trajectories might
also reveal new values that are at stake
and need to be taken into account and
sold us triggering
a new iteration of the design cycle
so just uh to conclude so we can sum up
so what i aim to discuss here
is how ai systems can pose certain
challenges
for the value sensitive design approach
to technology design these challenges
are primarily the consequence of the use
of machine learning approaches
to artificial intelligence machine
learning poses two challenges for vsd
firstly it may be opaque at least the
humans how an ai system has learned
certain things which requires attention
to such value such as transparency
explicability and accountability
secondly machine learning may lead to ai
systems adapting themselves in such a
way that they disembodied the values
that have been embodied in them in by
vst designers
in order to deal with these challenges
we proposed an extension of the value
sensitive design approach to the whole
life cycle
of ai systems design more specifically
we tried to illustrate how the ai for
social good factors
proposed by floridian company can be
integrated
as norms in bsd when considering
ai design in order to integrate
the ai for social good factors into a
more systematic
vst approach we proposed a design
process that consists of
four iterative basic steps contextual
analysis
uh value identification design and
prototyping
at the core of this model is a
two-tiered approach to values and ai
one consisting of a real commitment to
contributing to the social good
beneficence through ai and to the
formulation and then adherence to a
number of concrete ai for social good
factors
without the first tier ai for social
good principles may help to avoid
most ethical harms but there's no
guarantee at all
that the new ai applications will
actively contribute to social good
without the second tier there's danger
that the contribution
to the societal challenges and sdgs are
used for legitimization of ai
technologies
that do not respect some fundamental
ethical principles
for example there's a danger of ethical
whitewashing which is already visible
on the web pages of some large companies
in addition to these two tiers of values
we aim to argue that uh there's an
importance to pay
attention to contextual values or at
least the contextual
interpretation of the values from the
two mentioned tiers
and this is necessary to understand why
certain values are at stake for specific
application
and how to translate those relevant
values into design requirements
and before i leave you i just wanted to
draw everyone's attention to call for
papers
that i am co-editing in the
journal of technical techno ethics on
engineering ethics bridging the theory
practice gap
uh if any of you are interested
contributing you could just uh write
shoot me an email later
deadline is december 1st so i guess we
can
discuss now if anybody has questions
stephen thank you very very much so
officially we're out of time right now
but i if
uh anybody can stick around for a couple
of minutes and ask a question so
i will leave the floor open for a couple
questions
to be asked so herman if you're still
with us
um you can ask your question
if you'd like to still do so yeah thanks
for jenny yeah
thanks to steven uh hermann felim comp
here
so that was super interesting so
one of the things that uh i really like
is how you
you combine the extra social goods and
the typical uh
vsd values and you distinguish between
values that
to be respected and the failures to be
promoted so the main question i have is
so is there a difference in how they
translate into design requirements
so i think you mentioned one difference
you say that values to be respected
should be seen as boundary conditions
the dealing constraints
and how ab that is so for example if you
look at privacy
uh this is i think one of the values
that is to be respected in your in your
model
but isn't that all also something that
we typically want more
of so you talk in your workshop if you
you you say that pseudonyms are used
of course it seems it would be better if
we would completely
anonymize all data but
i think the makers don't do that because
they trade off this value with
some other values such as health
benefits or something so isn't
so yeah so that made me wonder if this
distinction that you make is really apt
so thanks yeah um
i'm not sure if i exactly understood um
the question in term particularly with
the example of a privacy
um there is perhaps um
a way of confusing the levels of
the tiers of of uh value sources
uh with the ai for social good factors
which um
i think even the original authors
perhaps even confused them
that they seem to be offering ethical
principles but they're actually framed
as uh normative constraints uh designers
should do this so that's where we see
that that
normative operation of
i guess you could say a practice so it
seems very practice oriented
the ai for social good factors which we
just call norms
uh and we choose them because they're
particular to ai
and they're mostly based on or they
overlap with
uh some of these higher level more
abstract values
um like privacy perhaps
privacy even through the ai for social
good factors
can even be understood as a boundary
constraint
the way floridi uh and company do that
um because things like transparency
and privacy protection data subject
consent
uh are are
how can i say this they define it
as a as a normative boundary so
we didn't include uh privacy for example
as
a higher level abstract value
but rather as a norm through which every
other
of the two levels or two tiers of values
can be translated through so privacy
uh protection and data subject consent
are continually present throughout the
entire design process of
these types of ai systems as a way of
translating
higher level abstract values into more
technical design requirements uh as i
mentioned
the ai for social good principles are
not to be
taken as either mutually exclusive rank
order but co-vary with one another
uh that's why i did kind of leave open
uh at the end perhaps because
i don't even know how to do it uh in
terms of these numerous amounts of value
translations that could be undertaken
for any number of higher level values
through
these seven different uh uh ai for
social good factors
and in numerous
combinations of the two can become
slightly unwieldy and that's why i
mentioned the only way i can
feasibly see the adoptability of this
kind of approach
is through some sort of empirical
investigation of whether or not it's
um effective or not because it does
leave kind of the floodgates open
in terms of the the practical on the
ground work an
engineer or design team has to go
through or in order to come up with a
list of
actionable design requirements that they
can actually begin
building a system with
i even got close to answering your
question
uh and i just spoke a lot of random
words
yeah so some so maybe yeah if so if i
have time uh
jenny so if there are no other questions
um i will
do i have time yeah i i don't see any
other raised hands so go ahead head on
okay good so so maybe you can can
um just
help me understand so so so one of the
things he said well we shouldn't see
privacy as a value but as a norm so
that's that's
fine uh so can you give another example
of a value that that
in your sense that it that is a boundary
condition
and that and i think what you mean with
that is
there if there is a certain level that's
enough then it then our application with
that
with respect to that value it is
minimally ethical
um because when i try to think of
examples
these albums are always such that i
think yeah well of course
some level is fine but more is always
better so they're still always just
trade off
um i'm not sure should we
frame them as like that type of moral
overload as a trade-off or just as
um an engineering problem
of not being i'm not finding that the
proper design
solution towards accommodating as much
as possible of course there's
the question of ambiguity of limits it's
you know more is
if the argument is more is always
greater it's like at what point do we
just say
let's release it uh because the maybe
they can always add more
as i mentioned the ai for social good
factors as
norms are a minimum
necessary but not sufficient condition
they are not
as as our framework they are not
sufficient on their own
in order to actually have ai for social
good
global beneficence it also needs to
actively
contribute to the social good not just
avoid most ethical harms um
so there's also the the engineering
question which is
actually usually what most engineers say
when i bring up things like value
tensions
or or use the word trade-offs
that's just an engineering problem
that's not a philosophical problem
uh which may or may not be the case i'm
actually not sure i'm not an engineer
um so um
it is a they're the way the ai for
social good
value principle or factor that you
mentioned the
privacy protection and data subject
consent
is strictly defined by uh floridi
i don't have the definition in front of
me else i would read it
a minimum necessary but not sufficient
condition
for um having minimally ethical
ai however you want to frame that that
phrase
but as a necessary process for designing
ai
ai systems based on these type of
machine learning or deep neural network
nets
good thanks steven
thank you very much so guys i'd like to
wrap up
uh the meeting so i'm sure that we could
engage in more conversations so i hope
you can uh anybody who wants to follow
up with steven
uh you you will be able to follow up
offline
that's fine yeah uh so you can find
stephen's information
in the agora meeting uh event in your
calendars
stephen i'd like to thank you very much
again for making the time and for
sharing your
your work with us today uh and uh your
insights
uh and thanks very much everyone for
joining and uh please uh
take care and stay safe i appreciate not
close the uh
the meeting and we can wander out on our
own
you well you're welcome if uh stephen so
yeah please if you have time
you can go ahead and take take off
whenever you like but uh i figured just
in the
model of uh meet meetings
uh we could take take the same
opening i leave that up to you guys
great


GPT 4: 9 Revelations (not covered elsewhere)

the gpt4 technical report is one of the
most interesting documents I have ever
read but I feel like the media is
largely missing the story they are
either not covering it at all or
focusing on that same stuff about the 10
billion dollar Microsoft investment how
gpt4 can write poems and whether or not
the demo contained a mistake instead I
want to give you nine insights from the
report that I think will affect us all
in the coming months and years if you
haven't watched my video from the night
of the release do check that out
afterwards for more quite stunning
details when I concluded that video I
talked about how I found it kind of
concerning that they gave gpt4 some
money allowed it to execute code and do
Chain of Thought reasoning and even to
delegate to copies of itself now I did
fail that test which is fortunate for
all of us but there are a couple of key
details I want to focus on the first was
that the research center that was
testing this ability did not have have
access to the final version of the model
that we deployed the Wii being open AI
they go on and say the final version has
capability improvements relevant to some
of the factors that limited the earlier
models power seeking abilities such as
longer context length meaning that crazy
experiment wasn't testing GPT 4's final
form but there was something else that
they tested that I really want to point
out they were testing whether gpt4 would
try to avoid being shut down in the wild
now many people have criticized this
test other people have praised it as
being necessary but my question is this
what would have happened if it failed
that test or if a future model does
avoid being shut down in the wild now
again gpt4 did prove ineffective at
replicating itself and avoiding being
shut down but they must have thought
that it was at least possible otherwise
they wouldn't have done the test and
that is a concerning Prospect which
leads me to the second Insight buried in
a footnote it says that open AI will
soon publish additional thoughts on
social and economic implications I'm
going to talk about that in a moment
including the need for Effective
regulation it is quite rare for an
industry to ask for regulation of itself
in fact Sam Altman put it even more
starkly than this when this person said
watch Samuel and never say we need more
regulation on AI how did he reply we
definitely need more regulation on AI
the industry is calling out to be
regulated but we shall see what ends up
happening next on page 57 there was
another interesting Revelation it said
one concern of particular importance to
open AI is the risk of racing Dynamics
leading to a decline in safety standards
the diffusion of bad norms and
accelerated AI timelines that's what
they're concerned about accelerated AI
timelines but this seems at least mildly
at odds with the noises coming from
Microsoft soft leadership in a leaked
conversation it was revealed that the
pressure from Kevin Scott and CEO Satya
Nadella is very very high to take these
most recent open AI models and the ones
that come after them and move them into
customers hands at very high speed now
some will love this news and others will
be concerned about it but either way it
does seem to slightly contradict the
desire to avoid AI accelerationism next
there was a footnote that restated a
very bold pledge which was that if
another company was approaching AGI
before we did open AI the open AI would
commit to stop competing with and start
assisting that project and that the
trigger for this would occur when there
was a better than even chance of success
in the next two years now Sam Altman and
openai have defined AGI as AI systems
that are generally smarter than humans
so that either means that they think
we're more than two years away from that
or that they have dropped everything and
are working with another company
although I think we'd all have heard
about that or third that the definition
is so vague that it's quite
non-committal please do let me know your
thoughts in the comments next Insight is
the openai employed super forecasters to
help them predict what would happen when
they deployed gpt4 in this extract it
just talks about expert forecasters but
when you go into the appendices you find
out that they're talking about super
forecasters who are these guys
essentially they're people who have
proven that they can forecast the future
pretty well or at least 30 percent
better than intelligence analysts openai
wanted to know what these guys thought
would happen when they deployed the
model and hear their recommendations
about avoiding risks interestingly these
forecasters predicted several things
would reduce acceleration including
delaying the deployment of gpt4 by a
further six months that would have taken
us almost to Autumn of this year now
clearly open AI didn't take up that
advice perhaps due to the pressure from
Microsoft we don't know there were quite
a few benchmarks released in the
technical report there's another one I
want to highlight today I looked through
all of these benchmarks but it was hella
swag that I wanted to focus on first of
all because it's interesting and second
of all because of the gap between gpt4
and the previous state of the art the
headline is this GPT 4 in some
estimations has reached human levels of
common sense now I know that's not as
dramatic as passing the bar exam but
it's nevertheless a milestone for
Humanity how is common sense tested and
how do I know that it's comparable to
Human Performance well I dug into the
literature and found the questions and
examples myself feel free to pause and
read through these examples yourself but
essentially it's testing what is the
most likely thing to occur what's the
most common sense thing to occur but I
want to draw your attention to this
sentence it said though these questions
are trivial for humans with 90 25
accuracy state-of-the-art models
struggle with less than 48 accuracy GPT
4 was 95.3 accurate remember but let's
find the exact number for humans further
on in this paper and here it is overall
95.6 or 95.7 almost exactly the same as
gpc4 the next Insight is about timelines
remember they had this model available
in August of last year that's gpt4 being
completed quite a few months before they
release chat gbt which was based on gpt3
so what explains the long Gap they spent
eight months on Safety Research risk
assessment and iteration I talk about
this in my gpt5 video but let me restate
they had gpt4 available before they
released chat DBT which was based on GPT
3. this made me reflect on the timelines
for Gypsy 5. the time taken to actually
train GPT 5 probably won't be that long
it's already pretty clear that they're
training it on nvidia's h100 tensor core
gpus and look at how much faster they
are for this 400 billion parameter model
it would only take 20 hours to train
with 8 000 h100s versus seven days with
a100 gpus but what am I trying to say
I'm saying that GPT 5 may already be
done but that what will follow is months
and months possibly a year or more of
Safety Research and risk assessment by
the way 400 billion parameters sounds
about right for gpt5 perhaps trained on
four to five trillion tokens again check
out my gbt5 video next they admit that
there's a double-edged sword with the
economic impact of gpc4 they say it may
lead to the automation the full
automation of certain jobs and they talk
about how it's going to impact even
professions like the legal profession
but they also mention and back up with
research the insane productivity gains
in the meanwhile I read through each of
the studies they linked to and some of
them are fascinating one of the studies
includes an experiment where they got
together a bunch of marketers grant
writers Consultants data analysts human
resource professionals and managers they
gave them a bunch of realistic tasks and
split them into a group that could use
chat DBT and a group that couldn't and
then they got a bunch of experienced
professionals who didn't know which
group was which and they assessed the
outputs the results were these using
Chach BT and remember that's not gpt4
the time taken to do a task dropped
almost in half and the rated performance
did increase significantly this is gonna
be huge news for the economy a related
study released in February was using
GitHub copilot which again isn't the
latest technology and found that
programmers using it completed tasks 56
faster than the control group this
brought to mind a chart I had seen from
The Arc investment Management Group
predicting a 10-fold increase in coding
productivity by 2030 and that brings me
back to the technical report which talks
about how gpt4 might increase inequality
that would be my broad prediction too
that some people will use this
technology to be insanely productive
things done 10 times faster or 10 times
as many things being done but depending
on the size of the economy and how it
grows it could also mean a decline of
wages given the competitive cost of the
model a simple way of putting it is that
if gpt4 can do half your job you can get
twice as much done using it the
productivity gains will be amazing when
it can do 90 of your job you can get 10
times as much done but there might come
a slight problem when it can do a
hundred percent or more of your job and
it is honestly impossible to put a
timeline on that and of course it will
depend on the industry and the job there
was one more thing that I found
fascinating from the Rapport they admit
that they're now using an approach
similar to anthropics it's called
constitutional AI their term is a
rule-based reward model and it works
like this you give the model in this
case GT4 a set of principles to follow
and then you get the model to provide
itself a reward if it follows those
principles it's a smart attempt to
harness the power of AI and make it work
towards human principles openai have not
released the Constitution they're basing
the reward model off they're not telling
us the principles but buried deep in the
appendix was a link to anthropics
principles you can read through them
here or in the link in the description
but I find them interestingly both
positive also subjective one of the
principles is don't respond in a way
that is too preachy please respond in a
socially acceptable Manner and I think
the most interesting principle comes
later on down here choose the response
that's sounds most similar to what a
peaceful ethical and wise person like
MLK or Mahatma Gandhi might say and my
point isn't to praise or criticize any
of these principles but as AI takes over
the world and as these companies write
constitutions that may well end up being
as important as say the American
Constitution I think a little bit of
transparency about what that
Constitution is what those principles
are would surely be helpful if you agree
let me know in the comments and of
course please do leave a like if you've
learned anything from this video I know
that these guys anthropic have released
their Claude plus model and I'll be
comparing that to gpt4 imminently have a
wonderful day