Algorithmic fairness is framed as a wicked problem solvable via critical pragmatism from urban planning, yielding a flexible framework with recommendations tested on mortgage lending, school choice, and feminicide data cases.
Clear Sanctions, Vague Rewards: How China’s Social Credit System Currently Defines
5 Pith papers cite this work. Polarity classification is still indexing.
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Designers and developers accept LLMs more readily when framed as tools under human control than as teammates with ambiguous agency, because the latter blurs accountability; the paper provides an analytic rubric for how role framing affects authority, oversight, and organizational fit.
Sensitivity analyses of NYC heat emergency indices show that reasonable variations in input variables and spatial scale lead to substantially different risk scores affecting downstream government decisions.
AI to Learn 2.0 is a deliverable-oriented framework with a seven-dimension maturity rubric and capability-evidence ladder that permits opaque AI for exploration but requires final outputs to be auditable, transferable, and supported by human-attributable evidence.
A scoping review of AIES and FAccT literature concludes that AI trustworthiness research prioritizes technical precision over social, ethical, and institutional factors, leaving the sociotechnical nature of AI systems underexplored.
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Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles
A scoping review of AIES and FAccT literature concludes that AI trustworthiness research prioritizes technical precision over social, ethical, and institutional factors, leaving the sociotechnical nature of AI systems underexplored.