Practitioner-derived taxonomies of conversational artificial intelligence (AI) workflow errors show low inter-rater agreement among human coders, leaving open whether the instrument is ill-specified or the judgments are inherently difficult. We delivered a locked eight-category workflow-error taxonomy verbatim, under s...
D. Austria, B. McCollister, J. Lindsey et al.· medRxiv· 0 citations
Incident management (IM) has evolved over recent decades to cover an ever-expanding array of hazards and systems. Barring real-life experience, exercises are a key tool in developing an effective IM program. As part of enterprise resilience and operational readiness, IM practitioners design exercises to understand and...
Laura Fraade-Blanar, J. Graves, A. Clark-Ginsberg et al.· 0 citations
TRACE (Tracking Reliability of AI-generated Conversational Evidence), a practitioner-audit framework for evaluating the downstream workflow reliability of conversational AI, is presented, suggesting taxonomy legibility under standardized conditions even where human judgment diverged.
D. Austria, B. McCollister, J. Lindsey et al.· medRxiv· 0 citations
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