Measuring AI harms with multidimensional Lorenz Zonoids

Current AI risk management models are insufficient because they focus primarily on compliance and likelihood, rather than the real-world severity and multidimensional nature of harms. This limitation prevents effective prioritization of interventions. A new methodology proposes using multidimensional Lorenz Zonoids and Gini indices to model harm data, which is typically ordinal and multidimensional, thereby enabling a more effective risk assessment that considers harm severity.

The development of robust methods for measuring and assessing AI harms is critical for effective governance and responsible deployment of AI systems. Accurately quantifying the severity and multidimensional nature of AI harms will enable better strategic decisions regarding risk mitigation and intervention priorities across various sectors.

Existing AI risk management models are primarily compliance-driven and provider-centric, lacking focus on the severity of harms.

Forward consideration, not a verified fact.