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P E E R T O P E E R : I L T A ' S Q U A R T E R L Y M A G A Z I N E | F A L L 2 0 2 3
This complexity makes AI governance a challenging
task, requiring a multidisciplinary approach and a deep
understanding of both the technology and its societal
implications.
In addition to these challenges, AI governance also
involves addressing issues related to data quality and
integrity. AI systems are only as good as the data they
are trained on. If the data is biased or inaccurate, the AI
system's outputs will also be
biased or inaccurate. A more
complete understanding of
bias must take into account
human and systemic biases.
Therefore, ensuring data
quality and integrity is a critical
aspect of AI governance
Another key aspect of
AI governance is ensuring
that AI systems are used in a
manner that respects human
rights and democratic values.
This includes ensuring that
AI systems do not infringe on
individuals' privacy, do not
discriminate against certain
groups, and do not undermine
democratic processes. It
also includes ensuring that
individuals have the right to challenge decisions made by
AI systems and to seek redress if they are harmed by these
decisions.
However, developing effective AI governance
frameworks is a complex task that requires balancing
various competing interests. On the one hand, there is a
need to protect individuals and societies from the potential
harms of AI. On the other hand, there is a need to promote
innovation and economic growth. Striking the right
balance between these interests is a key challenge in AI
governance.
The Regulatory Response
In response to these challenges, Europe and other
countries are attempting to establish governance principles
for data and AI. The European Union's General Data
Protection Regulation (GDPR),
for example, has set a global
standard for data protection,
introducing stringent rules
around consent, transparency,
and the right to be forgotten.
Similarly, the EU's proposed
Artificial Intelligence
Act aims to create a legal
framework for AI, establishing
requirements for transparency,
accountability, and human
oversight.
However, these efforts
are proving difficult due to the
complex, global, and rapidly
evolving nature of digital
technologies. Data and AI do
not respect national borders,
making it challenging to enforce
regulations in a global digital economy. Moreover, the pace
of technological change makes it difficult for regulations to
keep up, leading to a constant game of regulatory catch-up.
In addition to these challenges, there are also
concerns about the potential for regulatory fragmentation.
As different countries and regions develop their own
regulations for data and AI, there is a risk of creating
a patchwork of conflicting rules that could hinder the
F E A T U R E S
"The pace of
technological
change makes
it difficult for
regulations to
keep up."
