IEEE Technology and Society Magazine - December 2016 - 24

in the abstract for their paper on the
subject that, "we are more likely to
agree on how machines ought to
treat us than on how human beings
ought to treat one another." Once
these agreements are reached and
codified, they could begin to lay the
basis for Principles leading to Ethical Standards or Best Practices.
A significant difficulty in creating
any ethical standards for AI comes
with the nature of something James
Barrat, author of Our Final Invention: Artificial Intelligence and the
End of The Human Era [8] calls, "The
inscrutability paradox." The basic
concept of this idea has to do with
the difference between what Barrat
refers to as "designed" or "evolved"
systems. Designed systems feature
transparent programming, where
humans write all the code to allow
for easier testing and scrutiny regarding ethical concerns. Evolved systems refer to programming outfitted
with genetic algorithms or hardware
driven by neural networks. Even
before AGI (or sentient AI) is reached,
self-perpetuating algorithms are far
from uncommon. Analyzing these
programs to allow for human intervention invokes the inscrutability paradox when their self-driving
behavior can no longer be accounted
for. The AI involved, even if intended
to be "friendly" in nature, cannot
be broken down for ethical analysis because it has evolved outside
of human intervention from when
it was developed. As Barrat notes,
"This means that instead of achieving a humanlike superintelligence, or
ASI, evolved systems or subsystems
will ensure an intelligence whose
'brain' is as difficult to grasp as ours:
an alien. That alien brain will evolve
and improve itself at computer, not
biological, speeds."
This is no trivial matter. In simplistic terms, it means turning systems
off that have programming directives to stay on may become impos-

called, Inadvertent Algorithmic Cruelty. As it turns out, Facebook's Year
in Review feature posted a picture of
Meyer's daughter in his feed, not recognizing that she had passed away
six months before. Meyer's response
points out the need for ethical reasoning behind the systems already
driving so much of our lives:

sible. Not because an evil spirit has
overtaken an operating system, but
because the program is maximizing
its efficiency through a logic programmers would not be able to ascertain.
"Being cynical, for ethics within
all branches of technology most
researchers think of themselves as
ethical and think about people writing about ethics as either being obvious or pompous. So in a field like
AI, where it's very difficult to build
intelligent systems, the concern that
your system might be too intelligent
and pose a risk hasn't been too high
on people's agenda." This is a quote
from an interview I did with Stuart
Russell, author of one of the most
widely used textbooks on AI, Artificial Intelligence: A Modern Approach
[9]. I quoted Russell before where as
a reminder he said, "instead of pure
intelligence, we need to build intelligence that is provably aligned with
human values" [10].
I am greatly encouraged to hear
that this thought leader feels AI programming needs to be aligned with
human values even with "unintelligent AI systems." This means, as
he notes, changing the goals of the
field to incorporate the perspective
that human values provide outside
of the general notion of intelligence.
A recent event involving Facebook
algorithms provides a great example
of what I mean in regards to the
importance of aligning human values with algorithms. In December of
2014, Facebook introduced a feature
called Year In Review that allowed
users to see their top pictures and
posts from the past twelve months
based on friend's clicks and likes.
The algorithm creating the feature
also took a person's pictures and
posted them within a holiday frame
featuring happily dancing cartoon
characters. On Christmas Eve, 2014,
Eric Meyer, author and founder of
web design conference, An Event
Apart, wrote a post for his blog
IEEE Technology and Society Magazine

Algorithms are essentially
thoughtless. They model certain decision flows, but once
you run them, no more thought
occurs. To call a person "thoughtless" is usually considered a
slight, or an outright insult;
and yet, we unleash so many
literally thoughtless processes
on our users, on our lives, on
ourselves [10].

By definition algorithms are
thought less and heartless. It doesn't
make sense to try and define code
by human values in this regard,
since they're completely separate
paradigms. This is why Stuart
Russell's work regarding Inverse
Reinforcement Learning (IRL) is so
compelling. During our discussion,
I mentioned the well-known example
from AI ethics about an algorithm
designed to make paper clips. While
the program seems harmless enough
in and of itself, if it were set to create paper clips at all costs, it might
harness electrical power from nearby buildings or hoard other natural
resources needed by humans to satisfy its primary directive. As compared
to a simplistic, "AI machine gone
rogue" scenario, the example is
used to show people the importance
programming plays in designing
autonomous machines.
Russell, however, in our interview
points out that goals for humans
exist within the context of how we've
already lived our lives up to the point
we receive a new goal. "When I tell
a human to make paper clips, that's
∕

DECEmber 2016

Table of Contents for the Digital Edition of IEEE Technology and Society Magazine - December 2016