Crowdsourced metaphors show rising anthropomorphism and warmth toward AI that predict trust and adoption, with notable demographic differences.
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A multimodal CNN on 87,547 Vogue images classifies fashion houses at 78.2% top-1 accuracy, decades at 88.6%, and years at 58.3% with 2.2-year mean error, and shows texture and luminance carry most of the house-identity signal.
Analysis of Canada's Federal AI Register reveals it frames AI as reliable internal tooling by obscuring sociotechnical elements like human discretion, turning transparency into performative compliance.
Qualitative review of ASB 018 and five audit reports shows that compliant audits of DNA genotyping software often fail to establish usage boundaries despite observed failures due to vague language in the standard.
LLMs generate lower-quality STEM explanations for marginalized student profiles in Indian and American contexts, with intersectional compounding producing gaps of up to 2.55 grade levels.
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Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures
Analysis of Canada's Federal AI Register reveals it frames AI as reliable internal tooling by obscuring sociotechnical elements like human discretion, turning transparency into performative compliance.