Humans naturally avoid options filled with question marks. If an AI therapy platform isn’t transparent about how it uses your data or how it arrives at its suggestions, users balk. Machine learning might offer groundbreaking solutions, yet if it feels murky—like a black box with uncertain outcomes—adoption stalls.
Leading projects address this by offering interpretability layers, even in generative models. Imagine a system that explains: “Based on your last three journal entries, I’m suggesting mindful breathwork,” or a retrieval-based summary of relevant research the AI is drawing from. Clarity dissolves that fog of uncertainty and helps people trust—and benefit from—what the system has to offer.
Conclusion: Lift the veil on how decisions are made. By demystifying our AI engines, we invite engagement instead of avoidance, and forge a more confident path into uncharted therapeutic domains.
Credit to Quantum for doing much of the heavy that inspired the thinking behind this post. I've been going through my feature roadmap with a different lens and it's paid dividends. They have a fascinating playbook on behavioural economics free to download. It's refreshing to see such innovation in a world where easy and quick wins seem to take most of the pie.
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