Key Insights

• •

  Design and research are tightly intertwined; it's often impossible to tell where one ends and the other begins.

• •

  Machine learning models primarily predict based on past data rather than interact dynamically with the environment.

• •

  Intelligent systems involve multi-agency contexts where both humans and machines have decision-making power.

• •

  Unlike static software, AI systems are dynamic, learning and evolving based on interactions and data.

• •

  Design research for AI must be future-focused and speculative, addressing not just usability but cultural values and trust.

• •

  Bias in AI is complex, involving mathematical, statistical, cognitive, and human biases requiring nuanced mitigation.

• •

  Researchers and designers need high AI and data literacy to influence data inputs and model fairness, not just outputs.

• •

  Explainability in AI is challenging, especially with neural networks, necessitating governance and 'glass box' approaches.

• •

  Design anthropology and participatory methods help uncover unspoken human practices important for automated systems.

• •

  Speculative prototypes (provocation prototypes) and perpetual synthesis support ongoing learning about future AI use.

Notable Quotes