We explored the feasibility of predicting, using individually-tailored artificial intelligence (AI) models, substance use and craving from Fitbit data collected in free-living conditions. Most substances examined acutely raise heart rate, and craving is known to often involve autonomic arousal. Because heart rate res...
Ali Kargarandehkordi, Agnik Banerjee, Yi-Nan Sun et al.· npj Biosensing· 0 citations
Ref referential uncertainty is studied in a collaborative puzzle task where a human Helper instructs an AI Worker to place pieces, showing that a separately elicited belief distribution over candidate pieces is better calibrated and better discriminates correct from incorrect placements than raw action-token probabilit...
Christian Poelitz, F. Doshi-Velez, Siân Lindley· 0 citations
This work designed two scaffolded interfaces around the same negotiation scaffold: one presented a completed AI analysis, while the other supported user-directed, incremental development, which elicited a broader repertoire of analytic requests and lower subjective effort.
Zi-Lin Ma, Suzi Jazmati, Marco Chimenton et al.· 0 citations
When an AI algorithm makes decisions that affect more than one person, aligning it becomes a problem of social choice: how should people's divergent preferences about system behavior be reconciled and aggregated into a single coherent model? The standard approach to aligning frontier AI models$\unicode{x2013}$reinforce...
Zachary Wojtowicz, Michelle Si, F. Doshi-Velez et al.· 0 citations
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