We present a new optimization-based approach to modeling bias in evaluation processes ((OptProg)). Our model has two parameters, risk averseness  $\alpha$  and resource-information trade-off  $\tau$ , which are well documented to lead to evaluation biases in a number of contexts. We show that it can generate rich classes of output densities (Theorem 3.1) and discuss how the output densities depend on the two parameters (Section 3). Empirically, we demonstrate that the densities arising from our model have a good fit with the densities of biased evaluations in multiple real-world datasets and a synthetic dataset; often, leading to a better fit than models of prior works [90, 61] (Table 1). We use our model as a tool to evaluate different types of bias-mitigating interventions in a downstream selection task-illustrating how this model could be used by policymakers to explore available interventions (Figure 1 and Appendix L.1); see also Appendix B. Our work relies on the assumptions in prior works that there are no differences (at a population level) between  $G_{1}$  and  $G_{2}$ ; see, e.g., [89, 61, 40]. If this premise is false, then the effectiveness of interventions can be either underestimated or overestimated which may lead a policymaker to select a suboptimal intervention. That said, if all the considered interventions reduce risk aversion and/or resource constraints, then the chosen intervention should still have a positive impact on the disadvantaged group. Our model can be easily used to study multiple socially-salient groups by considering a group-specific risk-aversion parameter and a group-specific information constraint. For example, if two groups  $G_{1}, G_{2}$  overlap, then we can consider three disjoint subgroups  $G_{1} \cap G_{2}$ ,  $G_{1} \backslash G_{2}$  and  $G_{2} \backslash G_{1}$ . Our model of evaluation processes considers scenarios where candidates are evaluated along a single dimension. It can also be applied - in a dimension-by-dimension fashion - to scenarios where individuals are evaluated along multiple dimensions, but the evaluation in any dimension is independent of the evaluation in other dimensions. Modeling evaluation processes involving multiple correlated dimensions is an interesting direction. While we illustrate the use of our model in a downstream selection task, utilities generated from biased evaluation processes are also used in other decision-making tasks (such as regression and clustering), and studying the downstream impact of evaluation biases on them is an important direction. Moreover, the output of or model can be used by policymakers to assess the impact of interventions in the supermodular set aggregation setting, where the utility of the selected group is more than the sum of the individuals. Our model cannot be directly used to understand the effect of interventions in the long term. Additional work would be required to do so, perhaps as in [38], and would be an important direction for future work. Finally, any work on debiasing could be used adversarily to achieve the opposite goal. We need third-party evaluators, legal protections, and available recourse for affected parties - crucial components of any system - though beyond the scope of this work.