Key Insights
Opportunities and Risks
Now is the time to develop policy frameworks and ideas that work
AI is most often an enabler, not the solution itself
AI systems risk replicating and exacerbating existing biases, power imbalances and systemic inequalities…
…But AI can also address inequalities, if designed with that goal in mind
Safety risks associated with emergent capabilities are inherently challenging to manage
AI accountability and oversight infrastructure is nascent
Pathways and Ideas
Equitable data is a prerequisite for equitable AI
AI-related expertise and skills are needed across all sectors of society
Broader participation in the development of AI systems is crucial, albeit hard to achieve at scale
Building public trust in AI is essential for delivering benefits at scale
Agile governance is needed for long term ecosystem alignment and accountability
What We Learned
Working with organisations with specific expertise in each topic enriched the discussions
Pre-reading was influential in how conversations were framed
A range of organisations had advantages and trade-offs
Imagining the future productively is hard
Recency bias means that generative AI can dominate conversations
Connections were forged and ideas generated between groups