Key Insights

• •

  Generative AI does not think or understand; it excels at pattern recognition and filling in blanks like a souped-up mad lib.

• •

  AI design requires a paradigm shift from building products to designing dynamic behaviors within multi-agent ecosystems.

• •

  UX designers must move beyond traditional user-device interaction design to address machine learning's stochastic and contextual outputs.

• •

  Generative AI models have no moral code and can generate harmful outputs without human-guided guardrails and feedback loops.

• •

  Red teaming, adversarial testing, and safety classifiers are essential tools to proactively identify and mitigate AI risks.

• •

  Data and model cards documenting dataset provenance and model training are crucial for auditing bias and risk in AI systems.

• •

  Automation bias can cause humans to overtrust AI outputs, requiring design strategies that clearly signal uncertainty and keep users in control.

• •

  Machine learning models can unlearn harmful behaviors given correct feedback, but timely monitoring is critical before embedded biases solidify.

• •

  Mindful AI design entails co-design, simulations of multiple futures, and iterative testing with diverse users to understand AI behavior in context.

• •

  UX professionals hold unique responsibility and agency to embed ethical, equitable practices in AI design and deployment.

Notable Quotes