so when I was a teenager growing up in
Nigeria
I was not directing music videos I did
the style however to build an artificial
intelligence I know I know
I was taking computer science and logic
courses at local university and I
figured it would be easy being a 15 year
old to just create an AI that could
reason and argue logically I was
inspired by the same dreams the early
pioneers of artificial intelligence had
that we could we could create this super
mind and intelligence of pure logic and
objectivity and that this super mind
would be free of human traits traits
like subjectivity and bias I was working
with our guidance with really really old
computers and with irregular electricity
if you’ve been to Nigeria you might know
what that’s about
so let’s just say I was less than
successful I did however go on to study
in the United States and do a lot more
research as an engineer on artificial
intelligence around 2013 I was reminded
of my dreams of objective AI when I
heard of a Harvard professors
experiments
Latanya Sweeney at the University of
Harvard University was exploring how
search engine had change when you look
up different types of names when she
looked up names more predictive of being
black she got ads for criminal justice
services
so she’d look up names like DeSean or
even Latonya and she get ads like has
DeSean been arrested it’s not funny but
he’s kind of funny do you need a bail
bondsman when she looked up names less
predictive of being black so names like
Jeffrey or Emma not so much eventually
she calculated that black identify names
worth 25% more likely to generate ad
suggestion that the person had an arrest
record even when they did not but these
are algorithms serving up ads can
algorithms be prejudiced can a piece of
software be racist can it be sexist
learning algorithms are recipes for
teaching AI how to learn modern AI
learns by consuming vast amounts of data
they learn by spotting hidden
correlations and patterns that humans
cannot see they learn by applying
neutral math equations but the more
researchers have looked the more they
found examples of artificial
intelligence systems producing
significantly biased outcomes and by
bias we mean generally violations of
ethical or social norms for example a
recent study found that search engines
show more ads for higher paying jobs to
men than to women they’ve also been
there was a recent study last year
showing extensive bias in algorithms
used in the criminal justice system so
just using AI does not eliminate bias in
decision-making but how exactly do these
biases get into our algorithms in the
first place modern AI is only as good as
the data on which is trained AI eat
and processes data and use the patterns
it finds in the data to make future
decisions now these decisions could be
as trivial as distinguishing between
cats versus dogs or they could be
extremely important an insurance company
might use artificial intelligence to
help you decide who is insurable versus
who is not insurable consider the case
of an artificial intelligence trying to
find the best nurses if we decide to
feed this system stereotypical training
data so data consisting mostly of female
nursing profiles then chances are the
system would more competently judge
future female candidates as better fits
unless it’s carefully designed not to do
so the system isn’t being inherently
sexist it’s simply learning the biases
present in our data and applying them
more consistently into the future now
occasionally AI develops a case of what
we might call our food poisoning back
when IBM was trading up the future AI
jeopardy champion that’s Watson they had
to feed Watson vast amounts of data and
then somebody decided it might be a good
idea to feed Watson the urban dictionary
so after ingesting all that it’s just a
colorful data Watson developed a little
bit of a swearing habit
he began in inserting our weighted four
letter words in his responses now
personally I would have loved to watch
that version of Watson on Jeopardy but
the moral here is we need to be careful
what we feed our algorithms on our AI
and if n is AI is not going away
AI already determined is what you see on
your Twitter feed
what you might want to watch on your own
on Netflix who you might want to date
and it’s going to keep making more and
more of these decisions in the future
because the amount of data we have and
we create is vast and it just keeps
growing artificial intelligence and
algorithms present the only viable way
of making sense of this much data so if
you want better medical diagnosis if you
want fewer car crashes or if you want to
improve the quality of human lives and
prevent needless suffering more
generally you’re going to need AI
systems that can learn quickly tackle
complicated problems and make or inform
hard decisions but we also want these
systems to play fair so it’s great they
begin to get behind or at least discard
this illusion that AI modern-era is
objective or infallible especially now
as AI is ascendant and we can all get
behind this idea that we do not want AI
to make decisions that run contrary to
our values but how exactly do we teach
artificial intelligence to abide by
social or ethical norms and who gets to
decide what norms who gets to decide
what is fair to me these are fascinating
questions and researchers have started
looking at a few approaches to tackling
these questions so first we could
disclose to consumers when AI is making
decisions that affect them
top-down government regulation may not
be desirable or even feasible or
effective
as a way to tackle in biasing algorithms
making consumers aware is an easy first
step this also makes us more aware of a
thousand little ways in which artificial
intelligence affects our lives and more
aware of our choices as consumers of
services that rely on automated
decisions second we could create
processes for consumers to appeal
automated decisions they’d be appealing
to humans of course third we could have
more human oversight when artificial
intelligence is making decisions in
high-risk domains this includes domains
like defense or the criminal justice
system these are being policy solutions
so far there are a couple of technical
fixes in the works for example we’ve
been exploring ways of making AI models
more transparent more observable other
researchers have been exploring
exploring ways of making AI models
explain yet decisions the idea behind
these approaches these technical
approaches is to create a trail of
breadcrumbs so that we can follow the
trail of omi called intermediate
inferences forward from the inputs all
the way through to the final decisions
basically show your work or we can
follow the decisions the trail backward
from the decisions all the way down to
underlying causes or reasons these
solutions they help us see where AI bias
exists but they don’t always fix them
and more fundamentally don’t get the
more fundamental question of what
qualifies as bias in specific domains as
an engineer I fully own my tendency to
think of or focus on technical
virtuosity as the way to tackle and
solve problems just as I try to do as a
teenager all the way back then
but I’m not convinced that we can just
engineer our way out of this particular
problem because the problem is less
about the technology AI it’s more about
all the social and cultural context in
which we apply the technology at the
moment we have AI been applied in fields
as diverse as medicine law criminology
and even education each of these fields
have different norms different ethics an
AI is like a very smart child it’s not
always clear on the context of his
decisions for example in many state it
is perfectly legal to discriminate on
the basis of demographic characteristics
when you’re setting auto insurance rates
young and male drivers tend to be
riskier to insure at least so I’m told
but the ability to calibrate this kind
of risk is crucial for insurance on the
other hand it’s illegal to discriminate
on the basis of demographic
characteristics when you’re issuing
mortgages context determines the
relevant norms and engineers cannot
always teach AI how it ought to behave
in every single application we need to
work closer with social scientists so
lawyers economists anthropologist
linguist and I can tell you from
personal experience working across these
different fields is difficult we train
differently and this is not a joke we
really do speak different languages at
my day job at the rank corporation we
try to use as an example we try to use
artificial intelligence models to study
behaviors on social networks this
includes behaviors like radicalization
political polarization and even
cyberbullying what you might consider
fair or normative in any of those
applications will depend on what you’re
focused on if you focused on
radicalization then you might not think
it’s fair that we treat every
opinion expressed on social media
exactly alike here’s the crux of the
problem we have created artificial
intelligence in our own image and this
is not a compliment
AI to easily reflect human shortcomings
it finds in our data streams it
amplifies our flaws it’s going to take
the full range of human intelligence to
correct for these for the shortcomings
but if you want to build better fairer
societies we need AI systems that
reflect and amplify the better parts of
our nature thank you
Nigeria
I was not directing music videos I did
the style however to build an artificial
intelligence I know I know
I was taking computer science and logic
courses at local university and I
figured it would be easy being a 15 year
old to just create an AI that could
reason and argue logically I was
inspired by the same dreams the early
pioneers of artificial intelligence had
that we could we could create this super
mind and intelligence of pure logic and
objectivity and that this super mind
would be free of human traits traits
like subjectivity and bias I was working
with our guidance with really really old
computers and with irregular electricity
if you’ve been to Nigeria you might know
what that’s about
so let’s just say I was less than
successful I did however go on to study
in the United States and do a lot more
research as an engineer on artificial
intelligence around 2013 I was reminded
of my dreams of objective AI when I
heard of a Harvard professors
experiments
Latanya Sweeney at the University of
Harvard University was exploring how
search engine had change when you look
up different types of names when she
looked up names more predictive of being
black she got ads for criminal justice
services
so she’d look up names like DeSean or
even Latonya and she get ads like has
DeSean been arrested it’s not funny but
he’s kind of funny do you need a bail
bondsman when she looked up names less
predictive of being black so names like
Jeffrey or Emma not so much eventually
she calculated that black identify names
worth 25% more likely to generate ad
suggestion that the person had an arrest
record even when they did not but these
are algorithms serving up ads can
algorithms be prejudiced can a piece of
software be racist can it be sexist
learning algorithms are recipes for
teaching AI how to learn modern AI
learns by consuming vast amounts of data
they learn by spotting hidden
correlations and patterns that humans
cannot see they learn by applying
neutral math equations but the more
researchers have looked the more they
found examples of artificial
intelligence systems producing
significantly biased outcomes and by
bias we mean generally violations of
ethical or social norms for example a
recent study found that search engines
show more ads for higher paying jobs to
men than to women they’ve also been
there was a recent study last year
showing extensive bias in algorithms
used in the criminal justice system so
just using AI does not eliminate bias in
decision-making but how exactly do these
biases get into our algorithms in the
first place modern AI is only as good as
the data on which is trained AI eat
and processes data and use the patterns
it finds in the data to make future
decisions now these decisions could be
as trivial as distinguishing between
cats versus dogs or they could be
extremely important an insurance company
might use artificial intelligence to
help you decide who is insurable versus
who is not insurable consider the case
of an artificial intelligence trying to
find the best nurses if we decide to
feed this system stereotypical training
data so data consisting mostly of female
nursing profiles then chances are the
system would more competently judge
future female candidates as better fits
unless it’s carefully designed not to do
so the system isn’t being inherently
sexist it’s simply learning the biases
present in our data and applying them
more consistently into the future now
occasionally AI develops a case of what
we might call our food poisoning back
when IBM was trading up the future AI
jeopardy champion that’s Watson they had
to feed Watson vast amounts of data and
then somebody decided it might be a good
idea to feed Watson the urban dictionary
so after ingesting all that it’s just a
colorful data Watson developed a little
bit of a swearing habit
he began in inserting our weighted four
letter words in his responses now
personally I would have loved to watch
that version of Watson on Jeopardy but
the moral here is we need to be careful
what we feed our algorithms on our AI
and if n is AI is not going away
AI already determined is what you see on
your Twitter feed
what you might want to watch on your own
on Netflix who you might want to date
and it’s going to keep making more and
more of these decisions in the future
because the amount of data we have and
we create is vast and it just keeps
growing artificial intelligence and
algorithms present the only viable way
of making sense of this much data so if
you want better medical diagnosis if you
want fewer car crashes or if you want to
improve the quality of human lives and
prevent needless suffering more
generally you’re going to need AI
systems that can learn quickly tackle
complicated problems and make or inform
hard decisions but we also want these
systems to play fair so it’s great they
begin to get behind or at least discard
this illusion that AI modern-era is
objective or infallible especially now
as AI is ascendant and we can all get
behind this idea that we do not want AI
to make decisions that run contrary to
our values but how exactly do we teach
artificial intelligence to abide by
social or ethical norms and who gets to
decide what norms who gets to decide
what is fair to me these are fascinating
questions and researchers have started
looking at a few approaches to tackling
these questions so first we could
disclose to consumers when AI is making
decisions that affect them
top-down government regulation may not
be desirable or even feasible or
effective
as a way to tackle in biasing algorithms
making consumers aware is an easy first
step this also makes us more aware of a
thousand little ways in which artificial
intelligence affects our lives and more
aware of our choices as consumers of
services that rely on automated
decisions second we could create
processes for consumers to appeal
automated decisions they’d be appealing
to humans of course third we could have
more human oversight when artificial
intelligence is making decisions in
high-risk domains this includes domains
like defense or the criminal justice
system these are being policy solutions
so far there are a couple of technical
fixes in the works for example we’ve
been exploring ways of making AI models
more transparent more observable other
researchers have been exploring
exploring ways of making AI models
explain yet decisions the idea behind
these approaches these technical
approaches is to create a trail of
breadcrumbs so that we can follow the
trail of omi called intermediate
inferences forward from the inputs all
the way through to the final decisions
basically show your work or we can
follow the decisions the trail backward
from the decisions all the way down to
underlying causes or reasons these
solutions they help us see where AI bias
exists but they don’t always fix them
and more fundamentally don’t get the
more fundamental question of what
qualifies as bias in specific domains as
an engineer I fully own my tendency to
think of or focus on technical
virtuosity as the way to tackle and
solve problems just as I try to do as a
teenager all the way back then
but I’m not convinced that we can just
engineer our way out of this particular
problem because the problem is less
about the technology AI it’s more about
all the social and cultural context in
which we apply the technology at the
moment we have AI been applied in fields
as diverse as medicine law criminology
and even education each of these fields
have different norms different ethics an
AI is like a very smart child it’s not
always clear on the context of his
decisions for example in many state it
is perfectly legal to discriminate on
the basis of demographic characteristics
when you’re setting auto insurance rates
young and male drivers tend to be
riskier to insure at least so I’m told
but the ability to calibrate this kind
of risk is crucial for insurance on the
other hand it’s illegal to discriminate
on the basis of demographic
characteristics when you’re issuing
mortgages context determines the
relevant norms and engineers cannot
always teach AI how it ought to behave
in every single application we need to
work closer with social scientists so
lawyers economists anthropologist
linguist and I can tell you from
personal experience working across these
different fields is difficult we train
differently and this is not a joke we
really do speak different languages at
my day job at the rank corporation we
try to use as an example we try to use
artificial intelligence models to study
behaviors on social networks this
includes behaviors like radicalization
political polarization and even
cyberbullying what you might consider
fair or normative in any of those
applications will depend on what you’re
focused on if you focused on
radicalization then you might not think
it’s fair that we treat every
opinion expressed on social media
exactly alike here’s the crux of the
problem we have created artificial
intelligence in our own image and this
is not a compliment
AI to easily reflect human shortcomings
it finds in our data streams it
amplifies our flaws it’s going to take
the full range of human intelligence to
correct for these for the shortcomings
but if you want to build better fairer
societies we need AI systems that
reflect and amplify the better parts of
our nature thank you
Please follow and like us: