MBR ISSUE 43

SPECIAL FEATURE: POLITICO CONNECTED
Malta Business Review
One example of that trade-off is clickbait. It
may tap into a reader's immediate desire to
read the story behind a juicy headline, but
in the longer term it can skew the type of
information people receive.
This requires governments and businesses
to assign accountability, participants noted.
Businesses need to keep track of every
development step, because decisions made
from an AI's conception will dictate its
evolution. Governments, instead, need to
make clear that developers will be liable if
anything goes wrong.
"Then transparency will follow," said one.
"Yes, machine-learning is something that
can do things you don't expect. But systems
aren't just like one person trying to be Godlike,
systems are lots of pieces. … It is our
responsibility, if we're going to deploy these
technologies, to be willing to make the
liabilities that we come to."
2. Don't reinvent the wheel; adapt it
AI may be a constantly moving target, but
it doesn't require an entirely new policy
framework. Governments can start with
established national and international
standards, such as human rights law and
impact assessments, participants said.
"It's not about actually regulating a
technology," one participant said, pointing to
the U.K.'s strong existing regulations. "There
are lots of values already imbued in our law
and we just need to make sure that the AI is
compatible with that."
“Internationally, human rights law already
provides an ethics framework that allows for
trade-offs in difficult situations," a second
speaker said. "The question needs to be …
how do we translate all the human rights
legislation that's been built up over decades
into stuff that we can actually, practically use
when it comes to machine learning?"
In boardrooms, governments should push
executives to think about responsible AI in the
same way they're increasingly thinking about
climate change and sustainability, another
said. Environmental impact assessments,
for example, could provide blueprints
for algorithmic or technology impact
assessments, someone else noted.
3. Power and jobs to the people
AI is consolidating power and wealth into the
hands of those with the necessary skills, while
automation reduces the workforce needed to
run a business, speakers warned.
"We have companies making tens of billions
of dollars of profit with very few [employees]
"The energy sector, for
example, will need 10 to
15 years of planning and
data collection to gain
public trust
— that's never happened before, ever …
which actually is another problem," one
participant said.
Policymakers should work to rebalance the
power in this increasingly fragmented, AIpowered
world, the way consumer groups
and standards bodies help safeguard market
competition, another said.
"Even when you have got good consumer
choice, it just doesn't work — it doesn't drive a
good product for people, it doesn't mean you
get a good deal," the person said. "We have
to think, what's the ethical infrastructure we
might need that means individuals affected
have a bit more grit in the system?"
With fewer blue-collar jobs available, workers
will have to get used to the idea of "lifelong
learning," instead of relying on one skillset
for their entire careers, participants agreed.
And the education system will have to be
overhauled to support that.
4. Different values, different rules
The definition of responsible AI will
depend on a government's or society's
values — and those vary around the world,
speakers said. That will inevitably lead to
some regionalization.
"Human values are deeply, deeply political …
when we look at it on the international level,
we see just how divided the world is. China,
obviously," one participant said.
The U.K. and EU are already "highly aligned"
on many of those values, the person added.
But as AI spreads into the real world,
governments and companies will have to
think about what the technology they develop
in their countries and sell globally says about
their brand, and what that brand should say,
others added.
5. From research to the real world
Now the hard work starts: Jumping from
theoretical discussions to deploying AI
responsibly.
It's up to governments and developers to
identify those most affected by the changes
and prepare sectors that are lagging, such as
energy and manufacturing, speakers said. The
energy sector, for example, will need 10 to 15
years of planning and data collection to gain
public trust, one person said.
Asked what they would like to see over the next
18 months, another participant suggested
the creation of international standards that
provide a "mark of approval" for companies
without being too heavy-handed.
The U.K. is ahead of many others because
the conversation around AI ethics is already
mainstreamed, speakers agreed. Now it
needs to keep building political and public
awareness, understanding of how AI can be
used and consensus on the ethical trade-offs.
"We're all drinking the same Kool-Aid," said
one. "The problem is we need others to drink
the Kool-Aid. We need the public to come
with us." MBR
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