In Part 1 of this series, I said something that I’d thought I’d never say: after we’re coping with typical enterprise-scale AI systems, trust and safety is straightforward.
What?! Blasphemy!
Hear me out.
Yes, okay, it’s actually pretty hard. But the problem pales as compared to the trust and safety headache that’s the latest wave of generative AI. Here’s why.
Imagine you’re the CEO of an airline without an AI-based ticket pricing system. Your Chief of Staff runs into your office panting that some team of knowledge hotshots in your organization is hours away from a full-scale AI pricing system launch, but they were overheard saying, “I do not know how good this AI system is. Dunno how much revenue it makes or loses… however it seems useful, so let’s launch it.”
Heads will roll. Such a system’s reach and potential business impact is simply too massive for this level of sloppiness. You’ll likely fire everyone who had anything to do with this completely unhinged scenario and also you’ll be right to do it. In any case, because the CEO, ultimate responsibility for the airline’s success falls to you and eliminating this gaggle of clowns might be a no brainer given the inappropriate level of risk they almost subjected your enterprise to. The entire situation is criminally silly. Your organization is healthier off without them.
Say what you’ll about large organizations, however the one thing they have an inclination to be good at is avoiding anything that frivolously rocks the boat.
Typically, an issue like that’s smothered long before it reaches the CEO’s desk. Say what you’ll about large organizations, however the one thing they have an inclination to be good at is avoiding anything that frivolously rocks the boat. There’s a built-in preference for caution over gambling, which is why an enterprise-scale AI system typically only gets out of the gate if (1) it provably solves a problem provably well or (2) it has a provably low potential for harm (since the stakes are low, because errors wouldn’t be very embarrassing/painful, or because the appliance is of low strategic importance).
The straightforwardness of the AI system’s raison d’être is an especially powerful simplification tool.
Examples from an airline’s standpoint:
(1) An AI pricing system that’s rigorously launched in a gradual ramp-up and statistically tested to have a positive revenue impact of not less than x%.
(2) An AI pronunciation system that enables a gate agent hearken to a data-driven best guess about find out how to announce a passenger’s name to assist the agent out in the event that they’re unsure concerning the pronunciation. A system like that is hardly mission-critical and it comes with the upside of having the ability to tell the world you do AI without taking up much risk. Also, harmlessness is less complicated to attain when trained humans get to approve all of the output, so that you’d want your gate agents to make use of their judgment.
The purpose is that unless the trust and issues of safety are already minimized by the very nature of the appliance, an enterprise-scale AI system isn’t going to see the sunshine of day unless there’s proof that its upside is well worth the risk… and getting this sort of proof is inconceivable by definition when there’s no clarity concerning the value that the system provides.
Why does this make things easy? Since it implies that every mission-critical traditional enterprise-scale AI system (category (1)) tends to have:
- a comparatively straightforward use case statement
- a vision of what the intended “good behavior” for the system looks like
- a transparent, monolithic objective
- measurable performance
- well-defined testing criteria
- relative clarity about what could go flawed and thus which safety nets are needed
There are many challenges here too, like find out how to guarantee that a system like this plays nice with all the present enterprise systems (see my YouTube course for that and more), however the straightforwardness of the system’s raison d’être is an especially powerful simplification tool.
The important thing insight here is that the economics for enterprise-grade solutions are inclined to favor scale. Systems intended for deployment at scale often have a transparent purpose, else a wise leader sends them straight to the rubbish compactor. That’s why most enterprise-grade AI systems of the past decade were designed to do one very specific thing rather well at scale.
Most enterprise-grade AI systems of the past decade were designed to do one very specific thing rather well at scale.
It is a huge advantage for trust and safety. Huge! Sure, there’s loads of general reliability work to do to make sure that you retain your users protected when your system meets “the long tail” (the bizarre users), however it’s still loads easier to guard a varied group of users from a single-purpose, single-function system than to guard the identical group from a multi-purpose, multi-function system. And from most enterprises’ perspective, Generative AI systems are fundamentally multi-purpose and multi-functional.
That’s the important thing insight, so let’s repeat it:
It’s loads easier to guard a varied group of users from a single-purpose system than to guard the identical group from a multi-purpose system.
Should you’d like a greater understanding of this insight, proceed on to Part 3 of this series.
Alternatively, if this last insight is clear to you, then be at liberty to skip Part 3 and head straight to Part 4 where I explain why generative AI doesn’t include these same simplifying characteristics and what which means for AI regulation.
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P.S. Have you ever ever tried hitting the clap button here on Medium greater than once to see what happens? ❤️
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