Creating equitable access to transplantation remains one of the highest priorities in the field. The concept of justice has been a core value since the establishment of the Organ Procurement and Transplantation Network in 1984, mandating equitable access to transplantation in addition to the equitable allocation of organs. [1] Despite conscious efforts to ensure a fair process, recent data have shown Black candidates are 25% less likely to be waitlisted compared with Whites, even after adjusting for medical factors and social determinants of health. [2] Additional studies have found that ethnic minorities, women, and patients with low socioeconomic status were also less likely to be added to the waitlist and receive a transplant. [3,4] To assist in the complex process of transplant candidate selection, the Stanford Integrated Psychosocial Assessment for Transplant (SIPAT) was created and is a validated assessment tool with excellent inter-rater reliability. [5] The SIPAT has been shown to be associated with listing decisions but also immunosuppression nonadherence and allograft rejection. [6] This predictive tool can be a valuable asset regarding patient selection for transplant, but the impact on marginalized groups has not been well understood.In this issue of Liver Transplantation, Perry and colleagues aimed to understand the reliability and validity of the SIPAT and determine if this tool contributed to or ameliorated inequities in waitlisting conventionally marginalized candidates. As previous studies had shown, they found that internal reliability for the tool was good, including the composite score and within each of the 4 domains. However, when evaluating subgroup validity, they found that certain questions did not demonstrate robust validity or represent the domain it was addressing adequately, specifically across insurance type, race and ethnicity, and educational background. [7] They also found that multiple pairs of questions exhibited collinearity and did not improve the predictive performance of the tool, double penalizing candidates who scored poorly in those domains. Ultimately, despite the SIPAT having strong inter-rater reliability, the tool resulted in disproportionately higher scores for those with Medicaid, non-White candidates, and those without a college degree.