The paper suggests that companies developing high-risk AI systems should demonstrate their safety before deployment, arguing for proactive risk management. It proposes a risk management approach where developers must provide evidence that risks are below acceptable thresholds. The paper discusses technical and operational evidence for safety, comparing its approach to the NIST AI Risk Management Framework.
New estimators for generalized tail distortion (GTD) risk measures are proposed, based on first-order asymptotic expansions, offering simplicity and comparable or better performance than existing meth
Cyber risk presents significant challenges to society, yet its statistical behavior remains insufficiently understood. This paper analyzes three databases to study cyber risk dynamics. It identifies i
Effective transition to a sustainable economy requires robust emission reduction plans from companies, crucial for capital allocation and risk management. Transition disclosures serve as a market comp