Design to align with user’s mental model: Help users understand how a system works and how their actions affect it. Capture the user’s expectations, behaviour’s, and preferences to improve the AI system’s interactions with them.

Design for Trust and Reliance: Calibrating users’ trust is crucial for establishing appropriate reliance: teaching users to determine whether they are acceptable or if they should be modified or rejected.

Design for generative variability: Define visibility of multiple outputs be to users, and how might we help users organize and select amongst varied outputs?

Design for co-creation: Empower users to tailor outputs to their requirements by offering controls that allow them to shape the generative process and collaborate effectively with the AI.

Design for imperfection: Promote transparency by identifying potential imperfections and aiding users in understanding and managing outputs that may not meet their expectations.

Design Responsibly: Adopt a socio-technical perspective toward designing AI system responsibly. Understand how the mechanisms will improve the user’s experience, or address users pain points.

Conclusion: While we’re still in the initial stages of crafting user experiences that are both effective and secure with generative AI technologies, we’ve acquired valuable insights into what actions are advisable and which ones are not. 50

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