We introduce a natural, and widely applicable framework for fairness that relies on the available information. We develop algorithms for achieving a few different notions of fairness within a subjective framework, and in particular recently proposed concepts of fairness that are grounded in concepts of similarity and conditional independence. We argue that a suitable notion of similarity in the Bayesian setting is distributional similarity conditioned on the observations. For the latter, as independence is difficult to achieve uniformly in the Bayesian setting, we suggest a relaxation, for which we provide a small experimental demonstration.