AI AGENTS AS TRUSTED DIGITAL LABOUR
Discussion Points ▪ AI agents differ from large language models by having access to internal data, ability to execute workflows, and potential to act autonomously ▪ Trust is a significant concern when deploying AI agents in healthcare settings ▪ Debate around whether AI should augment human capabilities rather than replace humans entirely ▪ Data quality, governance and sovereignty are critical challenges for effective AI implementation ▪ Importance of defining clear scope and purpose for AI agents to build trust and manage expectations ▪ Need for human oversight in clinical decision-making contexts ▪ Potential for AI to help with administrative tasks, contact centres, and back-office functions Key Actions ▪ Start with small, low-risk use cases to build confidence before expanding to more complex applications ▪ Implement proper governance frameworks for both structured and unstructured data ▪ Ensure transparency in how AI agents make decisions and what data they access ▪ Consider cultural appropriateness when implementing AI in New Zealand context, particularly for Māori and Pacific communities ▪ Establish clear monitoring and audit capabilities for AI agent activities Additional Notes ▪ Healthcare organisations face resource constraints that AI could potentially address ▪ Significant variation in AI readiness across different healthcare organisations ▪ Current regulatory frameworks may not be well-suited for AI agent implementation ▪ Potential for agents to work together in complementary roles, similar to multidisciplinary teams ▪ Need to balance innovation with appropriate caution in clinical settings ▪ Importance of considering Te Ao Māori perspectives in AI implementation
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