When AI gives you an answer, can it tell you why?
In most cases, not really. Large language models and machine learning systems aren't built to explain their reasoning in a way that's auditable or traceable. They surface an output. The path to get there stays hidden.
In low-stakes settings, that's a tradeoff worth making. In healthcare and community services, it isn't. This is why explainability matters as a design principle, not just a nice-to-have.
It shapes how you deploy AI: use it to surface information, flag patterns, and reduce admin burden, not to make determinations on its own. It also shapes what you communicate to staff. People should understand what the AI is doing, where its boundaries are, and when to override it without friction.
Trustworthy AI in care and support settings isn't AI that's always right. It's AI that keeps the human in a position to be accountable.
What this means for health organizations
Use AI where its output can be checked: surfacing information, flagging patterns, drafting text for review. Keep determinations about people with staff who can explain them.
