Preparing AI for the unexpected: Lessons from recent IT incidents

AI modeling systems are no different. Neglecting the best practices of building models can lead to unrecoverable failures. Discover how the three-tiered framework of robustness, resiliency, and anti-fragility can guide your approach to creating AI infrastructures that not only perform reliably under stress but also fail gracefully when the unexpected happens.
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Show Notes

Technology, incidents, and why basics matter (00:00:03)

• While the recent Crowdstrike incident wasn't caused by AI, it's impact was a wakeup call for people and processes that support critical systems (Forbes, July 2024)
• As AI is increasingly being used at both experimental and production levels, we can expect AI incidents are a matter of if, not when. What can you do to prepare?

The "7P's": Are you capable of handling the unexpected? (00:09:05)

• The 7Ps is an adage, dating back to WWII, that aligns with our "do things the hard way" approach to AI governance and modeling systems.
• Let’s consider the levels of building a performant system: Robustness, Resiliency, and Antifragility

Model robustness (00:10:03)

• Robustness is a very important but often overlooked component of building modeling systems. We suspect that part of the problem is due to:
  • The Kaggle-driven upbringing of data scientists
  • Assumed generalizability of modeling systems, when models are optimized to perform well on their training data but do not generalize enough to perform well on unseen data.

Model resilience (00:16:10)

• Resiliency is the ability to absorb adverse stimuli without destruction and return to its pre-event state.
• In practice, robustness and resiliency, testing, and planning are often easy components to leave out. This is where risks and threats are exposed.
• See also, Episode 8. Model validation: Robustness and resilience

Models and antifragility (00:25:04)

• Unlike resiliency, which is the ability to absorb damaging inputs without breaking, antifragility is the ability of a system to improve from challenging stimuli. (i.e. the human body)
• A key question we need to ask ourselves if we are not actively building our AI systems to be antifragile, why are we using AI systems at all?

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