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HEWLETT PACKARD ENTERPRISE - Moral Code: The Ethics of AI - YouTube
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When people wonder what is
AI, they think about robots.
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Robots.
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Let's make a distinction.
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A robot itself is hardware.
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AI is software.
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What is Artificial Intelligence?
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Systems that mimic,
perhaps even go beyond,
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our own ability to think
and to process information.
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It determines who we want to date,
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whether we get a certain kind of loan,
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operating at all levels of our economy,
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in the political realm,
in the judicial system,
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employment practices, in
the educational system.
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All of these decisions help to determine
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how well or how poorly our lives go.
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And increasingly, AI is
involved in those decisions.
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We are right to be concerned
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when we are just now
learning to think about
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the social and ethical
implications of using AI.
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[Kirk] So many of our own stories
are about the relationship
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between the creation and the creator.
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So we're at this
inflection point right now.
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Suddenly all of this
data, these algorithms,
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and the computational power is available.
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That's really been the breakthrough.
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Things are becoming real
that we've talked about,
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we've dreamt about for decades.
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AI which can do a very
specific narrow task.
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That can recognize voice,
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navigate a car, translate language.
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And if this is going to
be the ubiquitous tool
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we're doing ourselves a disservice
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by not educating the
general population also
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on what is really going on right now.
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We have so many immediate
concerns about the kind of AI
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that will be developed that
we really need to devote
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most of our educational efforts
to those kinds of problems.
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Artificial intelligence uses
a massive amount of data
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to recognize certain kinds of patterns
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that are correlated with
a successful result.
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Hidden in these vast pools
of data that we're now using
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are remarkable opportunities
to run off the rails.
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So we have to be careful
about the data we give it.
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Data.
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Data can be bad because it's
not been properly labeled,
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because it's inaccurate,
because it has baked into it
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certain human biases that are
naturally a part of society.
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One clear example of data
bias is the use of algorithms
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to make decisions about
the fate of prisoners.
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If you have enough data
points, you should be able to
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judge a person's
likelihood of re-offending.
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But the problem is
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[John] if there is bias
hidden in the data,
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your outcome in the judicial
system may be very different
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than someone who is upper
middle class and white.
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Why is it projecting higher risk scores
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onto black defendants?
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It often turns out that the
algorithm itself isn't feeding
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on any data that's explicitly
labeled in terms of race.
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A zip code doesn't tell you
anything about a person's race.
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What the machine learns is,
ah, people in this zip code
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are more likely to be
criminal, when in fact,
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it may just be that they're
more likely to be black or poor
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or any other category that is
a subject of biased practices.
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These technologies will actually reinforce
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some of the worst aspects of our society,
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which is really a great
tragedy because the technology
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has the potential to actually
do exactly the opposite.
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Machines aren't racist.
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Machines aren't sexist.
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Machines aren't ableist or classist.
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We are.
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Our biases really get not only embedded
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in this one program, but
get scaled out rapidly.
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Silicon Valley has been a magnet
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for all the world's best,
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but it also tends to be
dominated by white males.
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AI has a white guy problem.
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It's going to be crucial for us
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to have different kinds of diversity
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to be part of the development of AI.
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Who builds these AI systems
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and what data are we
using in them is vital.
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What I see is a generation
that takes all kinds
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of life instructions from
this device whether it is
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where to go to eat Korean
barbecue or who to marry.
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It all comes magically out
of the palm of your hand,
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from an algorithm that
you know nothing about.
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Who's behind that screen?
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Who's on the other end of the wire?
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What are the motivations
of that algorithm?
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People no longer even think to
ask, it's just so convenient.
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[Kirk] That ability to predict
and then modify our behavior,
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perhaps even without us fully
understanding why or how.
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If you want the machine to
behave in an ethical manner,
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you have to know why it does what it does.
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Part of the transparency--
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Transparency.
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Is to be able to explain the logical steps
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that it went through to arrive
at a certain recommendation.
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When we are training these
machine learning algorithms,
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we're supplying it a
million pictures of cats.
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And we just say, "That's
a picture of a cat."
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We don't say, "That's a picture of a cat,
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"because of the ears and the tail,
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"the whisker and the nose," and all that.
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The machine itself starts to
recognize that such and such
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is a cat and such and such is not a cat.
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We don't actually know how
the circuit has reached
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that particular set of conclusions.
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Hot dog, not hot dog, whatever.
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That's funny.
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Fire missile, not fire missile.
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That's not funny.
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[John] The particular
nature of the technology
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that's being rolled
out, they're so complex
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and they operate on such large data sets
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that the function of the algorithm
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is largely eluding the designers.
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And maybe that's part of the regulation
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that will be built around AI.
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If you cannot explain what
this program has done,
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it just should not be
released to humankind.
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And there is this movement
starting where more and more
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people say, "Well, we need
to be able to audit this.
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"We need to be able to
understand what's going on."
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We're about to bump into things
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that we really have not thought
about as a human species.
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The notion that you can interact
with a bot on the internet
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and it can effect your propensity
to vote one way or another
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is a tremendous threat to democracy.
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Autonomous weapons is
a terrifying concept.
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Drones with smart algorithms
being turned into weapons.
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There is a legitimate fear that the power
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of artificial intelligence
will be disproportionately
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put in the hands of the already powerful.
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Should we give it to the rich,
'cause they can afford it?
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There is a popular view that
over a couple decade period
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we'll have no jobs at all.
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It's not a binary like,
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"Do I have a job in the future or not?"
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It's, "How is my job gonna change?"
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This isn't a bubble.
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AI is going to become a bigger and bigger
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and bigger part of everything.
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And this might be the way
that we get to the stars,
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get to the bottom of the ocean.
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Cure cancer, fix climate change.
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The world is getting better
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and these technologies are enabling that.
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At its best, it's going to
solve all of our problems.
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And at its worst, it's going to be
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the thing that ends humanity.
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We must develop responsible
and safe practices
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before the power gets too
unmanageable for us to control.
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Whose responsibility is it?
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Is it corporations?
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Government?
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Is it society?
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And it's really all of us.
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There would probably
scientists and technologists
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who would say, "I'm not gonna
let any consideration stop me
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"from my investigation
of the world we live in."
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We all have to together say,
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"Is that a road we want to go down?"
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Human responsibility for human power.
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[Curt] You shouldn't be okay
with us making all the rules.
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The new Manhattan Project
is not AI development;
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the new Manhattan Project is AI safety.
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The more that they take on characteristics
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of our own cognitive processes,
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the more that raises our
responsibility as their creators,
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as their trainers, to
constantly be vigilant
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and ask ourselves, "How
should I be doing this work?"
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Technology by itself is not good or bad.
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If AI is done right,
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it's all going to make us better humans.
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