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Fairness Hazard Analysis reveals that up to 27% of process elements in socio-technical systems may harbor fairness hazards, necessitating proactive mitigation strategies.
ACF changes significantly influence code quality, revealing critical insights into how developers can better govern autonomous coding agents.
LLMs in software engineering are mostly used for automation, not decision support, and suffer from reproducibility issues, revealing a critical gap in human-centered integration and transparency.
LLMs exhibit surprisingly inconsistent behavior when prompted with semantically identical requests that differ only in demographic group, and SCOPE reveals the extent of this bias across diverse topics and communicative intents.
LLMs can ace the facts but still whiff on the ethics, relational context, and broader implications when answering seemingly innocuous "sensitive" prompts.
