How Safe is AI?

Thoughts on the Market vor 2 Jahren

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4:34

Mike Canfield, Morgan Stanley’s Head of Europe Sustainability
Research, discusses why ensuring safe and responsible artificial
intelligence is essential to the AI revolution.

----- Transcript -----

Mike Canfield: Welcome to Thoughts on the
Market. I'm Mike Canfield, Morgan Stanley's Europe, Middle East
and Africa Head of Sustainability Research.

Today I'll discuss a critical issue on a hot topic: How safe is
AI?

It's Thursday 10th of October at 2pm in London.

AI is transforming the way that we live, work, and connect. It's
really got the potential at every level and aspect of society,
from personal decisions to global security. But as these systems
become ever more integrated into our critical functions – whether
that's healthcare, transportation, finance, or even defense – we
do need to develop and deploy safe AI that keeps pace with the
velocity of technological advances.

Market leaders, academic think tanks, NGOs, industry bodies,
intergovernmental organizations have all attempted to codify what
safe or responsible AI should look like. But at the most
fundamental level, the guidelines and standards we've seen so far
share a number of clear similarities. Typically, they focus on
fostering innovation in practical terms, as well as supporting
economic prosperity – but also asserting the need for AI systems
to respect fundamental human rights and values and to demonstrate
trustworthiness.

So where are we now in terms of regulations around the world?

The EU's AI Act leads the way with its detailed risk-based
approach. It really focuses on transparency as well as risks to
people and fundamental rights. In the USA, while there's no
comprehensive federal regulation or legislation, there are some
federal laws that offer some sector specific guidance on AI
applications. Things like the National Defense Authorization Act
of 2019 and the National AI Initiative Act of 2020. Alongside
those, President Biden's published an executive order on AI,
promoting safety, responsible innovation, and supporting
Americans and their rights, including things like privacy. In
Asia Pacific, meanwhile, countries are working to establish their
own guidelines on consumer protection, privacy, and transparency
and accountability.

In general, it’s very clear that policymakers and regulators
increasingly expect AI systems developers to adopt what we'd call
the socio-technical approach, focused on the interaction between
people and technology. Having examined numerous existing
regulations and foundational standards from around the world, we
think a successful policymaking approach requires the combination
of four core conceptual pillars.

We've called them STEP. That's Safety, Transparency, and Ethics
and Privacy. With these core considerations, AI can take a step –
pun intended – in the right direction. Within safety, the focus
is on reliability of systems, avoiding harm to people and
society, and preventing misuse or subversion. Transparency
includes a component of explainability and accountability; so,
systems allowing for future feedback and audits of outcomes.
Ethically, the avoidance of bias, preventing discrimination,
inclusion, and the respect for the rule of law are key
components. Then finally, privacy considerations include elements
like data protection, safeguards during operation, and allowing
users consent in data used for training.

Of course, policymakers contend with a variety of challenges in
developing AI regulations. Issues like bias, like discrimination,
implementing guardrails without stifling innovation, the sheer
speed at which AI is evolving, legal responsibility, and much
more beyond. At its most basic, though, arguably the most
critical challenge of regulating AI systems is that the logic
behind outcomes is often unknown, even to the creators of AI
models, because these systems are intrinsically designed to
learn.

Ultimately, ensuring safety and responsibility in the use of AI
is an essential step before we can really tap into ways AI could
positively impact society. Some of these exciting opportunities
include things like improving education outcomes, smart electric
grid management, enhanced medical diagnostics, precision
agriculture, and biodiversity monitoring and protection efforts.
AI clearly has enormous potential to accelerate drug development,
to advance material science research, to boost manufacturing
efficiency, improve weather forecasting, and even deliver better
natural disaster predictions.

In many ways, we need guardrails around AI to maximize its
potential growth.

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