Tag
This paper proposes a three-step framework for designing and reporting benchmarks for knowledge work AI, emphasizing alignment between benchmark tasks and real-world work activities. It derives 18 work activities from the O*NET database and analyzes three existing benchmarks (GDPval, OfficeQA Pro, APEX-SWE) to demonstrate gaps between benchmark scores and actual work capability.
This paper identifies the 'evaluation trap' where AI benchmarks inadvertently stabilize dominant paradigms by narrowing what counts as progress, and introduces Epistematics, a meta-evaluative methodology to ensure evaluation criteria discriminate true capability from proxy behaviors.