AI and the Underserved: Why Most Equity Conversations Miss the Point
The AI equity conversation focuses almost entirely on bias in training data. That's important, but it misses the more fundamental issue: who gets to use these tools at all.
The dominant conversation about AI and equity focuses on algorithmic bias. Is the facial recognition system less accurate for darker skin tones? Is the hiring algorithm penalizing candidates from historically Black colleges? Is the credit scoring model discriminating against certain zip codes?
These are real, important problems that deserve serious attention and regulatory intervention.
But I want to name a different, more fundamental problem that rarely gets the same attention: who can actually use these tools at all.
When we talk about AI equity, we're almost always talking about the equity implications for people who are already inside the AI economy — who are already being hired or fired, scored or assessed, by AI systems. We're asking: is the system fair to the people it's already affecting?
We're not often asking: who isn't inside the AI economy at all, and what does it mean for their futures that they're excluded from the productivity gains, the economic opportunity, and the capability augmentation that AI provides to those who can access and use it?
In Indianapolis, I've worked with small business owners who are already being priced out of their markets by competitors who use AI for everything from inventory management to customer service. The question isn't whether AI is biased against them. The question is whether they can participate at all.