Limited health literacy (HL) partially mediates health disparities. Measurement constraints, including lack of validity assessment across racial/ethnic groups and administration challenges, have undermined the field and impeded scaling of HL interventions. We employed computational linguistics to develop an automated and novel HL measure, analyzing >300,000 messages sent by >9,000 diabetes patients via a patient portal to create a Literacy Profiles. We carried out stratified analyses among White/non-Hispanics, Black/non-Hispanics, Hispanics, and Asian/Pacific Islanders to determine if the Literacy Profile has comparable criterion and predictive validities. We discovered that criterion validity was consistently high across all groups (c-statistics 0.82-0.89). We observed consistent relationships across racial/ethnic groups between HL and outcomes, including communication, adherence, hypoglycemia, diabetes control, and ED utilization. While concerns have arisen regarding bias in AI, the automated Literacy Profile appears sufficiently valid across race/ethnicity, enabling HL measurement at a scale that could improve clinical care and population health among diverse populations.