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Data exposure is situate as a central interaction problem in end-of-life ubiquitous computing and design implications for interpretive stability, care-context alignment, and existential support are derived.
This work investigated post-task workflows: editable, graph-based representations of an agent's completed execution that improve their understanding and error detection over a prompt-only condition, and found that validation succeeded mainly when users cross-checked across multiple evidence sources.
Simulations show that over-reliance on a weak AI is especially harmful, and that diversifying AI signals across users can better keep the crowd informative, and conclude with implications for understanding human-AI interaction in information spread and designing misinformation interventions.
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