Abstract: I use network models to explore how social injustice impacts learning in a community. First, I simulate situations where a dominant group devalues evidence from a marginalized group. I find that the marginalized group ends up developing better beliefs. This result uncovers a mechanism by which standpoint advantages for the marginalized group can arise because of testimonial injustice. Interestingly, this model can be reinterpreted to capture another kind of injustice—informational injustice—between industrial and academic scientists. I show that a subgroup of scientists can learn more accurately when they unilaterally withhold evidence.