The Cost-Benefit of Interdisciplinarity in AI for Mental Health

2025-10-21

AI Summary

Current AI-powered mental health chatbots predominantly rely on single-disciplinary inputs, lacking cross-disciplinary integration across the entire system lifecycle—thus failing to meet the EU AI Act’s stringent requirements for value alignment and regulatory compliance in high-risk AI systems. To address this, we propose a collaborative design framework integrating expertise from computer science, clinical psychology, ethics, and law, spanning requirement specification, model development, deployment validation, and continuous monitoring. Our approach embeds NLP capability assessment, dynamic ethical review, legal compliance mapping, and lifecycle governance mechanisms to balance safety, clinical utility, and regulatory adaptability. We quantitatively analyze cost–benefit ratios at critical collaboration junctures, identify optimal timing for interdisciplinary intervention, and specify the minimal viable team composition. This work establishes the first reusable, auditable, and scalable collaborative engineering paradigm tailored for high-risk AI-based psychological interventions.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Intelligent Robots: Embodied AI

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Examining cost-benefit trade-offs in interdisciplinary AI mental health chatbots
• Addressing limited disciplinary integration across chatbot development lifecycle
• Ensuring value-alignment and regulatory compliance through expert collaboration

Innovation

• Integrates diverse experts across chatbot lifecycle
• Ensures value-alignment with AI Act requirements
• Provides frameworks balancing interdisciplinary challenges