Public AI Benchmarks Are Broken, But Are Private Benchmarks the Answer?
This work argues for a hybrid public-private approach: keeping the benchmark content private to maintain integrity while making the benchmark design public to build trust, which offers a path toward trustworthy measurement of ever-growing data-intensive AI systems.
Sang T. Truong, S. Wang, Angelina Wang et al.
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