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