In this work, we develop a theoretical formulation for describing dissipative material behaviors in a stochastic setting, using the framework of Generalized Standard Materials (GSM). Our goal is to capture the variability inherent in the material model while ensuring thermodynamic consistency, by employing the mathematical framework of stochastic programming. We first show how average behaviors can be computed using the expected value of the free energy and dissipation pseudo-potentials. We then introduce the concept of a risk-averse effective measure, which provides both an optimistic and a pessimistic estimate of the uncertain material behavior. To this end, we utilize the Conditional Value-at-Risk, a widely used risk measure in mathematical finance. We also demonstrate how these concepts can be extended to variational problems at the structure scale, allowing us to compute the effective response of a structure composed of a stochastic material.