Is there a way to incorporate fairness in the opponent modeling component of an automated agent? Since opponent modeling plays an important role in a negotiation strategy, it is reasonable to research how fairness can be integrated into this component, as it influences the outcome of the negotiation. A first step towards finding an answer to this question is to define fairness and how this definition can be translated to algorithmic fairness. The next step is to investigate already available opponent models and assess whether their strategy can be considered fair or not. This paper analyses the process of Bayesian learning in the context of opponent modeling, and tries to reveal possible flaws or strengths that the model has embedded in it, with the hope to add relevant information in the area of automated negotiation.