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2018 saw a number of major technology
vendors – including Google and IBM –
publish AI ethical guidelines. Meanwhile, the
European Commission recently published its
Ethics Guidelines for Trustworthy AI.
These moves clearly signal that now is
the time for organisations to prioritise
their ethics efforts to ensure AI is being
applied appropriately – in other words,
that outcomes are fair, transparent, legal
and aligned with human values. Future
commercial performance and corporate
reputations will depend on enforcing
appropriate AI standards. At a bare
minimum, that will require a process
of oversight and evaluation, as well as
individual accountability.
The issue of trust and data assets
The UK government is striving to enable
sustainable and trusted data infrastructures
that maximise data use and value to the
benefit of the economy and society in general.
It’s a concept that underpins eGovernment,
as well as concepts like Smart Cities.
Working with the Open Data Institute,
the government is exploring the potential
of ‘data trusts’ that allow two or more
organisations to share data in a safe, fair
and ethical way so they can work together to
tackle issues at a local level – enabling open
collaboration models that reduce cost and
create value.
When it comes to the development and
application of AI, an ethical framework
should take into consideration three key
deployment areas:
• Creation: Does the AI use training
data that poses a significant risk to
an individual’s right to privacy? Is it
representative and does it contain historic
biases that could be perpetuated?
• Function: Are the assumptions used
by AI, and the processes that power
these, reasonable and fair? Can anyone
understand how AI works and audit how
a given output was created? Can you
protect against hacking or manipulation?
• Outcomes: Is AI being used to do
anything unethical? Has appropriate
oversight and evaluation been applied?
Who is ultimately responsible for
decisions made?
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The EU’s General Data Protection Regulation
(GDPR) represents the first step in a raft
of regulations that aim to establish clear
governance principles in relation to data.
The California Consumer Privacy Act (CCPA)
is set to take effect in 2020 and is widely
considered to be the most comprehensive
privacy legislation in the United States.
Organisations must get ahead of this fast-
evolving regulatory curve and integrate new
control structures and processes designed to
manage AI risks and ensure AI technologies
are used appropriately.
For Luciano Floridi, Chair of The Alan Turing
Institute Data Ethics Research Group and
Ethics Advisory Board, the success of AI will
depend on the use of well-curated, updated
and fully reliable datasets. For him, quality
and provenance – where the data comes
from – is critical.
When it comes to addressing legal
compliance and ethical issues such as
privacy, consent and other social issues, he
believes the answer lies in using synthetic
data that’s generated by AI itself.
In the foreseeable future, he predicts that
a move from using anonymised historic
data to entirely synthetic data will be key
to ensuring privacy or confidentiality is not
infringed at the development stage of AI
“
ORGANISATIONS
NEED TO ACT
NOW OR RISK
POTENTIAL
EXPOSURE TO
FINANCIAL
LOSS AND
REPUTATIONAL
DAMAGE IF THEIR
APPROACH TO AI
IS NOT ETHICAL.
Joanna Hu, Manager, Data Science
(Machine Learning) at Exabeam
solutions – although he acknowledges that
predictive privacy harms at the deployment
stage will still need to be managed carefully.
Action steps
Organisations need to act now or risk
potential exposure to financial loss and
reputational damage if their approach
to AI is not ethical. With regulators and
governments preparing to address the
AI ecosystem, establishing a dedicated
AI governance and ethics advisory board
that includes cross-functional leaders and
advisers should be the first priority.
When it comes to the development and
deployment of AI technologies, companies
will need to be confident these systems do
not unintentionally encode bias or treat
people – employees, customers, or other
parties – unfairly. That means putting tools,
processes and control structures in place
and ensuring that development teams are
appropriately trained on ethical AI practices.
Responsibility now falls on the shoulders
of data scientists and organisations to act
ethically and keep their finger on the pulse
when it comes to the evolving regulatory
landscape. The ability to take advantage of
AI technologies and capture potential future
value depends upon it. n
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