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Artificial intelligence (AI) agents promise to extend what users can accomplish beyond their existing expertise, but they also raise the risk of producing plausible-looking work that users may struggle to evaluate. We ask how task-relevant expertise and trust in AI shape agent-assisted work. To isolate these factors, w...
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.
It is found that federal court outcomes are shaped less by where AI has caused harms and more by which harms are cognizable under existing statutes, leading to certain AI harms remaining unresolved.
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