Why AI benchmarks fail to measure real-world performance — and what should replace them

Summary

The article argues that current AI benchmarking methods are fundamentally broken because they evaluate AI performance in isolation (task-level, static tests) rather than in the real-world, messy, human-centered environments where AI is actually deployed. While some progress has been made with dynamic evaluation methods, these still fail to account for the human teams and organizational workflows that shape AI's real-world impact. The author calls for a shift toward more human-centered, context-specific evaluation methods that measure AI's performance within the collaborative ecosystems where it operates.

Source

bskyWhy AI benchmarks fail to measure real-world performance — and what should replace themtechnologyreview.com