Human-AI Collaboration Metrics

On March 20th, two papers appeared on ArXiv within what looks like hours of each other, and together they sketch the outline of a conversation the AI research community is quietly starting to have. The first, "From Accuracy to Readiness," argues that the metrics we currently use to evaluate AI systems — accuracy scores, benchmark performance, the usual leaderboard currency — are the wrong instruments for measuring something far more consequential: whether humans and AI systems actually make better decisions together than apart. The second paper pushes further, drawing a line between what it calls cognitive amplification and cognitive delegation. The distinction matters more than it might sound. Amplification means the human stays in the loop, sharper for the assistance. Delegation means the human quietly steps back, deferring to the machine in ways that may not be visible until something goes wrong. Both papers propose metric frameworks to track which dynamic is actually happening in deployed human-AI systems.

If confirmed as a genuine research direction gaining traction, here is what this means. The entire edifice of AI evaluation — how companies benchmark models, how regulators assess risk, how enterprises decide whether a system is "ready" for deployment — rests on measuring what the AI does in isolation. These papers suggest that framework is not just incomplete but potentially dangerous, optimising for the wrong variable entirely. An AI that scores brilliantly on accuracy benchmarks could still be systematically eroding the judgment of the humans working alongside it, and no current metric would catch that happening. For enterprise AI buyers, this raises uncomfortable questions about tools already in production. For regulators drafting AI governance frameworks, it suggests a new class of audit requirement is coming. The cognitive delegation problem, in particular, has second-order effects that compound over time — organisations that delegate decisions upward to AI systems may find, years later, that the institutional knowledge needed to catch AI errors has quietly atrophied.

Watch for whether these frameworks get adopted or cited by applied AI labs rather than staying within academic circles — that would signal the field is taking the measurement problem seriously. Any regulatory body referencing "decision-readiness" or "cognitive delegation" metrics in guidance documents would confirm this is moving from theory toward policy.

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