Designing for failure, not success with AI/ML | by Sid | Jan, 2022


Designing for failure not success with AI/ML
Designing for failure, not success with AI/ML
Google image speech to text search
  1. Redirect them: Other times, people may enter search terms that aren’t completely wrong but still aren’t very useful.

Our goal should not be to design AI to succeed; it should be to design AI to avoid errors by the people who work with it.

AI is designed for humans, not for machines. It will become part of our daily lives — from shopping online to using a digital bank service — so if we’re going to design it, we need to make sure it works for everyone.

People from all walks of life should be able to use it without having special training. For example, if you’re designing an app for children with special needs, make sure it doesn’t rely on verbal input.

If people can’t see or hear your AI, they won’t use it. For example, make sure text-to-speech voices are gender-neutral and sound natural; don’t assume everyone wants their phone to talk to them like Siri or Alexa. It should be transparent.

AIs are optimized by humans to interact with humans in a way that maximizes their value to humans. The best example of this is an autonomous vehicle. An autonomous vehicle’s objective function is to minimize the number of accidents and fatalities involving its passengers. The safety of the passengers is prioritized over that of other people in accidents, pedestrians, bikers, even animals.

Autonomous Tesla car gif
  1. Guide people towards best practices
  2. Give people tools to recover from mistakes

Keep it simple. Don’t overthink it, don’t over-engineer it.

The design process becomes complex once you require that your system be both smart and safe (as we saw with self-driving cars). That said, one way to test if your system is capable of making errors is by testing for adversarial scenarios. Multiple times. Just as humans offer different points of attack/failure, so does the design.

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