Post 2: The Average Problem

Machine learning systems optimize for the mean.

Feed a neural network a dataset, ask it to minimize error across the population, and it learns to perform well on average. The problem: "average" often represents almost no one.

When you design AI for the average:

• 95% accuracy overall

• 87% accuracy for accented speakers

• 43% accuracy for people with dysarthria

You've optimized the system to perform best for people who need it least and worst for people who need it most.

Inclusive design requires inverting the target: optimize for the users with the most challenging needs. If you serve them well, you serve everyone well.

#AI #InclusiveTech #Accessibility