This paper explores how competitive pressure in AI development influences safety choices. Through a behavioral experiment and an evolutionary model, it demonstrates that unsafe development is primarily driven by fear of falling behind and strategic responses to competitors, rather than individual risk preferences alone.

This paper introduces a formal logic of inability as a first-class concept, extending Coalition Logic to systematically study what multi-agent coalitions cannot achieve. It establishes the modal properties of this explicit inability operator, highlighting its significance for reasoning about constraints and safety in AI systems.

The paper argues that high‑stakes AI systems threaten human agency and proposes a Causal‑Agency Framework that embeds causal modeling, uncertainty quantification, and actionable interfaces to restore user control.

This paper introduces a novel measurement framework to evaluate human-AI decision-making, shifting focus from mere model accuracy to the readiness of human-AI teams for safe and effective collaboration. It proposes a taxonomy of metrics and connects them to the Understand–Control–Improve lifecycle to assess calibration, error recovery, and governance in real-world deployments.