  Measurement of social phenomena is everywhere, unavoidably, in sociotechnical
systems. This is not (only) an academic point: Fairness-related harms emerge
when there is a mismatch in the measurement process between the thing we
purport to be measuring and the thing we actually measure. However, the
measurement process -- where social, cultural, and political values are
implicitly encoded in sociotechnical systems -- is almost always obscured.
Furthermore, this obscured process is where important governance decisions are
encoded: governance about which systems are fair, which individuals belong in
which categories, and so on. We can then use the language of measurement, and
the tools of construct validity and reliability, to uncover hidden governance
decisions. In particular, we highlight two types of construct validity, content
validity and consequential validity, that are useful to elicit and characterize
the feedback loops between the measurement, social construction, and
enforcement of social categories. We then explore the constructs of fairness,
robustness, and responsibility in the context of governance in and for
responsible AI. Together, these perspectives help us unpack how measurement
acts as a hidden governance process in sociotechnical systems. Understanding
measurement as governance supports a richer understanding of the governance
processes already happening in AI -- responsible or otherwise -- revealing
paths to more effective interventions.
