About this episode
David and Kate discuss the importance of better data for effective and trustworthy AI systems.
Continuing their examination of the assumptions underlying today’s dominant AI narrative, David and Kate explore what makes data useful, trustworthy, and meaningful. They discuss the limitations of extraction-based approaches to AI, the importance of local context and data ownership, and the challenges of building systems that can learn across diverse communities without centralising control. The conversation highlights why better data—not just more data—may be key to building more effective and trustworthy AI systems.
People in this episode
Hosts: David, Kate
Topics covered
- AI narrative
- data usefulness
- data ownership
- local context
- trustworthy AI
- community learning
Keywords
- AI
- data
- trustworthiness
- local context
- community
- extraction-based approaches
Mentioned in this episode
Organizations: IDEMS International
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