A repeatable worksheet and human-reviewed expansion process turns expert-elicited AI use cases into 107 grounded scenarios to support consistent human-centered evaluations.
ACM Transactions on Computer-Human Interaction, 27(5)
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Queer artists largely refuse and resist generative AI, seeing it as anti-relational and disruptive to the community-oriented, identity-forming nature of their art practices, with only limited acceptance for surreal image generation.
A statistical framework decomposes human annotation outcomes into four interpretable variation sources and extends classical measurement-error models to handle both shared and individualized notions of truth.
Proposes an interdisciplinary framework and taxonomy for responsible evaluation of AI mental health tools based on analysis of 135 publications identifying gaps in metrics, expert involvement, safety, and equity.
Industry markets AI agents for orchestration, creation, and insight, but a usability study with 31 participants reveals users face challenges from capability misalignment and lack of meta-cognition in tools like Operator and Manus.
Coding benchmarks misalign with agentic software engineering because they conflate model and harness, grade against single references, and provide no component-level iteration signals.
Calibrated LLM annotations of Schwartz human values in non-English social media text transfer to encoder models via soft-label training while retaining theoretical alignment and uncertainty signals.
Proposes AI-driven simulations for literary-historical experiments and reports preliminary text-generation results claiming the first limited in-distribution outputs matching human novels.
Context specification is a process that turns diffuse stakeholder perspectives into explicit definitions of properties, behaviors, and outcomes to guide context-aware AI evaluations.