Page 32 - AI Governance Day - From Principles to Implementation

AI Governance Day - From Principles to Implementation

• Government roles and responsibilities

– Cabinet-level oversight: there is debate on whether AI should have dedicated oversight
or be integrated into existing frameworks. Ensuring government responsibility for AI
harms and mandating transparency is critical.
– Cybersecurity and AI: the growth of cyberattacks underscores AI’s role as a defense
system. Addressing severe, state-sponsored attacks and determining responsibility
is necessary.

4�4 Theme 2: how do we implement AI governance frameworks?

4.4.1 Lagging laws, lagging tech: the AI governance paradox

In the rapidly evolving landscape of AI, a paradox has emerged: while regulation is often seen
as lagging behind technological advancements, there is an equally critical yet less recognized
issue where technology fails to keep up with regulatory demands. While existing regulatory
frameworks struggle to adapt to the pace of AI innovation, simultaneously, the current state
of technology and tools available does not allow for monitoring, checking, and controlling
AI systems. This gap poses risks and underscores the need for advancing tools capable of
ensuring effective governance.

AI governance includes the governance of data, the governance of algorithms, and the
governance of computing resources (compute).

• Governance of data: The governance of data refers to the policies, procedures, and
standards necessary to manage the lifecycle of data within AI systems. Data governance
ensures data quality, integrity, and security, which are essential for the reliable operation
of AI technologies. This includes establishing robust protocols for data collection,
storage, and sharing, and implementing privacy and security measures to protect
sensitive information. Data governance frameworks must address issues such as consent,
transparency, and accountability to maintain public trust and comply with regulatory
requirements. Moreover, data governance plays a crucial role in mitigating biases in
AI systems by promoting diverse and representative datasets, thereby enhancing the
fairness and accuracy of AI outcomes.
• Governance of algorithms: The governance of algorithms focuses on the ethical and
responsible development, deployment, and oversight of AI models and their decision-
making processes. This aspect of AI governance aims to ensure that algorithms operate
transparently, fairly, and without discrimination. It involves creating standards and
guidelines for algorithmic accountability, which include regular audits, performance
evaluations, and the ability to explain AI decisions to stakeholders. Algorithmic
governance also emphasizes the importance of ethical considerations, such as avoiding
unintended harmful consequences and ensuring that AI applications align with societal
values.
• Governance of compute: The governance of compute addresses the management and
oversight of computational resources required to develop, train, and deploy AI systems. As
AI models become increasingly complex and resource-intensive, the need for sustainable
and equitable access to computing power grows. Compute governance involves setting
policies for the efficient and fair allocation of computational resources, ensuring that
these resources are used responsibly and do not disproportionately favor well-resourced
entities over smaller or less funded organizations. Additionally, it includes considerations
for environmental sustainability, as the energy consumption of AI training and operations
has a significant ecological impact. By implementing strategies to optimize energy use
and reduce carbon footprints, compute governance aims to balance the advancement
of AI with the imperative of environmental stewardship.

22