Nowak, P.; Kowalski, J.; Lewandowski, T.

Public TCR--pMHC binding databases are heavily skewed toward a handful of well-studied HLA alleles---most prominently HLA-A*02:01, which covers $\sim$45\% of curated records---and toward patients from European-ancestry cohorts. Standard empirical risk minimization (ERM) trained on such data achieves strong pooled accuracy but routinely underperforms on rare alleles and underrepresented cohorts, creating systematic disparities that are invisible in single-metric benchmarks. We introduce \emph{FairTCR}, a group distributionally robust optimization (GDRO) framework that minimizes worst-group loss across HLA supertypes and cohort strata via online exponentiated gradient updates. FairTCR reduces the average--worst-group AUPRC disparity from 0.190 (ERM) to 0.098 on a curated VDJdb--IEDB benchmark, achieving a 48.4\% disparity reduction while maintaining competitive average AUPRC (0.432 vs.\ 0.431 for ERM). Per-HLA analysis shows that rare allele groups (B*08:01, B*44:02) gain up to 0.062 AUPRC points, directly improving the equity of computational pre-screening for underrepresented patient populations.