Constitutional AI & AI Ethics - Weekly Roundup

This work proposes ViD, a causally-inspired framework that analyzes attention mechanisms across five distinct patterns, revealing confounding effects from strong language priors and demonstrates that visual-to-language cross-attention effectively suppresses bias while preserving general reasoning capabilities and text generation quality.

The findings provide the first empirical evidence that seemingly"objective"physical descriptions can retain systematic gender associations in human interpretation, and uncover systematic patterns of model-human misalignment.

This analysis finds substantial variation in coverage: AI risks related to robustness, system security, and governance receive more attention than socioeconomic, environmental, and emerging risks, including multi-agent risks.

This work develops a Carbon-Aware AI Procurement Framework (CAAPF), a Green IS design artifact that operationalizes sustainable AI governance for enterprise procurement, and suggests that environmental responsibility and decision quality can be mutually reinforcing.