Artificial intelligence has been introduced as a way to improve access to mental health support. However, most AI mental health chatbots rely on a limited range of disciplinary input, and fail to integrate expertise across the chatbot's lifecycle. This paper examines the cost-benefit trade-off of interdisciplinary collaboration in AI mental health chatbots. We argue that involving experts from technology, healthcare, ethics, and law across key lifecycle phases is essential to ensure value-alignment and compliance with the high-risk requirements of the AI Act. We also highlight practical recommendations and existing frameworks to help balance the challenges and benefits of interdisciplinarity in mental health chatbots.

I read an interesting article about Google using their DeepMind AI system to improve their power usage efficiency by 15% – which adds up to hundreds of millions of dollars of savings. Of course, DeepMind has been a big investment for Google and finding areas for them to gain efficiency leads to immediate payback – not necessarily covering the entire investment, but savings that add up over time to those hundreds of millions. Like any good organization, I'm sure that Google started with metrics so they had a handle of not just what the costs were, but where the biggest cost impacts were occurring. As the saying goes, "you can't change what you don't measure". However, a lot of organizations get stuck in metrics mode and never get around to the work of actually optimizing – they are are always measuring but never changing.