How Do You Measure Ecosystem-Level AI Risks?

Model-level metrics are well-developed: accuracy, bias benchmarks, latency, confabulation rates. Application-level measurement is harder but tractable: user satisfaction, downstream decision quality, harm reports.

But ecosystem-level risks, algorithmic monoculture, information integrity decay, labor market disruption, lack clear measurement frameworks. The effects are diffuse, slow-moving, and defy attribution to any single system or actor.

Current approaches fall short:

• Aggregate statistics miss the distribution of impacts across populations
• Time-series analysis can’t establish counterfactuals (what would have happened without GAI?)
• Survey methods capture sentiment but not structural effects
• Academic studies lag the pace of deployment by years

What measurement approaches would make ecosystem-level risks visible before they become irreversible?

Related: 05-molecule—risk-dimension-model, 05-atom—algorithmic-monoculture-definition, 02-atom—information-integrity-definition